Beyond the Random Walk: A Guide to Stock Market Anomalies and Low Risk Investing
by Vijay Singal · 15 Jun 2004 · 369pp · 128,349 words
anomalies that do not have a reasonable explanation, especially if it appears that the mispricing has occurred just by chance. SELECTION BIAS Another bias that may creep into the discovery of mispricings is selection bias, that is, the sample may be biased in favor of finding the desired result. Assume you want to measure
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and the result is falsely attributed to the entire American population, including rural and less urban areas. In the case of stock market studies, a selection bias can creep in when the results arise from a certain part of the sample but seem to be representative of the entire market. For example
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skepticism, as spurious mispricings can surface for a variety of reasons, such as errors in defining normal return, data mining, survivorship bias, small sample bias, selection bias, nonsynchronous trading, and misestimation of risk. • Though anomalies should disappear in an efficient market, they may persist because they are not well understood, arbitrage is
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Beyond the Random Walk Chan, Louis K. C., Narasimhan Jegadeesh, and Josef Lakonishok. 1995. Evaluating the Performance of Value Versus Glamour Stocks: The Impact of Selection Bias. Journal of Financial Economics 38(3), 269–96. Chen, Honghui, Greg Noronha, and Vijay Singal. 2004. The Price Response to S&P 500 Index Additions
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and, 135, 142, 149 Internet resources, 160 merger arbitrage and, 197 Ownership Reporting System, 138 reporting requirements, 134, 136, 146–47, 149 345 346 Index selection bias, 12 self-attribution, 286 Seyhun, Nejat, 147 share repurchases, 136, 309–10, 317n4 Sharpe, William, 8 Sharpe ratios forward rate bias strategies, 276, 279 in
Statistics in a Nutshell
by Sarah Boslaugh · 10 Nov 2012
sample is biased, meaning it is not representative of the study population, conclusions drawn from the study sample might not apply to the study population. Selection bias exists if some potential subjects are more likely than others to be selected for the study sample. This term is usually reserved for bias that
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) of employees before and after the lecture series and finds that the series has been effective in increasing healthy behaviors and decreasing unhealthy behaviors. Solution Selection bias and nonresponse bias, both of which affect the quality of the sample analyzed. The reported average annual salary is probably an overestimate of the true
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and women who shop at the mall, let alone who live in the area? Quota sampling can also be subject to a particular type of selection bias, which is also a risk in convenience sampling. The data collector might approach people who seem most like himself (for instance in age) or who
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better able to recall events. Reliability How consistent or repeatable measurements are over time. Retrospective study A study of events that have already taken place. Selection bias Bias due to the way a sample is selected. Sensitivity In medicine and epidemiology, the probability that a person who has a disease will test
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. Variance A measure of the variability of a range of numbers, calculated as the mean squared difference from the mean. Volunteer bias A type of selection bias resulting from collecting data from a sample of volunteers. Index A note on the digital index A link in an index entry is displayed as
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Terms predictions of presidential elections and sample, Exercises publication bias, Quick Checklist recall, Information Bias recall bias, Glossary of Statistical Terms retrospective adjustment, Retrospective Adjustment selection, Bias in Sample Selection and Retention, Glossary of Statistical Terms social desirability, Information Bias, Glossary of Statistical Terms types of, Measurement Bias–Information Bias volunteer, Bias
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in Research Design–The Power of Coincidence, Descriptive Statistics–Extrapolation and Trends writing, Writing the Article–Writing the Article secondary data, Basic Vocabulary–Basic Vocabulary selection bias, Bias in Sample Selection and Retention, Glossary of Statistical Terms semantic differential scale, The Likert and Semantic Differential Scales–The Likert and Semantic Differential Scales
The Case Against Education: Why the Education System Is a Waste of Time and Money
by Bryan Caplan · 16 Jan 2018 · 636pp · 140,406 words
. Alon and Tienda 2005 and Titus 2004 present typical results. 88. Heil et al. 2014 reports that, after correcting for many preexisting student traits and selection bias, college selectivity at least does not raise completion probability. 89. My estimates also assume college quality has the same proportional effect on unemployment; i.e
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18 (3): 253–92. Frank, Robert. 1999. Luxury Fever: Money and Happiness in An Age of Excess. Princeton, NJ: Princeton University Press. Frazis, Harley. 1993. “Selection Bias and the Degree Effect.” Journal of Human Resources 28 (3): 538–54. ———. 2002. “Human Capital, Signaling, and the Pattern of Returns to Education.” Oxford Economic
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Enrollment at Public Colleges and Universities.” Educational Evaluation and Policy Analysis 33 (4): 435–57. Hendricks, Lutz, and Oksana Leukhina. 2014. “The Return to College: Selection Bias and Dropout Risk.” CESifo Working Paper Series 4733. Last modified April 5. http://ssrn.com/abstract=2432905. Henrichson, Christian, and Ruth Delaney. 2012. “The Price
Expected Returns: An Investor's Guide to Harvesting Market Rewards
by Antti Ilmanen · 4 Apr 2011 · 1,088pp · 228,743 words
specific and/or has no investment value for the future. Here is some color on each explanation. Data mining (also called data snooping, overfitting, or selection bias) Any interesting empirical regularities could be spurious (which does not mean that they all are). When thousands of academics and practitioners study the same historical
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0.5% to 0.8% lower than the U.S. premium for 1900–2009—consistent with mild selection bias. The authors argue that use of their world premium captures the bulk of any survivorship or selection bias given that the 19 countries in their database may have accounted for 90% of world equity market
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successful ones will eventually be reported while poorer performers will not. Empirically, it is clear that average returns are lower if backfill filters are used. • Selection bias. Funds mainly report for marketing reasons. They may opt out not only because of poor performance but also because an established fund is closed and
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”. Investable indices offered lower costs, better transparency, and better liquidity than non-index investments. However, top HFs had little incentive to participate, leading to adverse selection bias, and investable HF indices have consistently underperformed broader HF indices. After a brief detour I will return to other HF alternatives. Alphas, betas, alternative betas
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studies, I exclude currencies that were fixed or pegged to another currency, along with currencies that have or had significant capital controls. Due to this selection bias, I might overstate returns by excluding hyperinflated and/or defaulted markets that fell out of my universe. In practice, this has not been a major
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episodes why strategies work cyclical effects credit spreads growth seasonal regularities see also business cycles D/P see dividend yield data mining see also overfitting; selection bias data sources of time series data series construction day-of-the-week effect DDM see dividend discount model debt supercycle default correlations, CDOs default rates
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also at-the-money options; calls; puts out-of-the-money (OTM) options seasonal regularities tail risks volatility selling overconfidence overcrowding overfittingsee also data mining; selection bias overoptimism overreaction P/B ratio see price-to-book ratio P/E ratio see price/earnings ratio pairwise carry trading PE see private equity funds
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—1987 asset richening disinflation financialization future trends Great Moderation leveraging market vs. state nextyears recession 2008—2009 reversible sustainable ten trends securities see also TIPS selection bias see also data mining; overfitting self-attribution self-deception selling volatility sentiment indicators share buybacks Sharpe ratios (SRs) active investing BAB balancing stock—bond portfolios
Science Fictions: How Fraud, Bias, Negligence, and Hype Undermine the Search for Truth
by Stuart Ritchie · 20 Jul 2020
’s a wicked problem, and more pervasive in studies than we’d like to think. See also Marcus R. Munafò et al., ‘Collider Scope: When Selection Bias Can Substantially Influence Observed Associations’, International Journal of Epidemiology 47, no. 1 (27 Sept. 2017): pp. 226–35; https://doi.org/10.1093/ije/dyx206
Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking
by Foster Provost and Tom Fawcett · 30 Jun 2013 · 660pp · 141,595 words
be greater than zero, so: That is, the expected donation (lefthand side) should be greater than the solicitation cost (righthand side). A Brief Digression on Selection Bias This example brings up an important data science issue whose detailed treatment is beyond the scope of this book, but nevertheless is important to discuss
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who happened to have been selected in the past be a good sample from which to model the general population? This is an example of selection bias—the data were not selected randomly from the population to which you intend to apply the model, but instead were biased in some way (by
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to donate in some past campaign will be biased towards those who donate less. Fortunately, there are data science techniques to help modelers deal with selection bias. They are beyond the scope of this book, but the interested reader might start by reading (Zadrozny & Elkan, 2001; Zadrozny, 2004) for an illustration of
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dealing with selection bias in this exact donation solicitation case study. Our Churn Example Revisited with Even More Sophistication Let’s return to our example of churn and apply
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with the offer. Hopefully this would be a representative sample of the customer base to which the model was applied (see the above discussion of selection bias). Developing our Data Understanding, let’s think more deeply about each of these in turn. How can we obtain a sample of such customers who
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with a similar, but not identical, offer in the past. If this offer had been made to customers in a similar situation (and recall the selection bias concern discussed above), it may be useful to build a model using the proxy label. [64] The expected value decomposition highlights yet another option. What
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implementations. Morgan Kaufmann, San Francisco. Software available from http://www.cs.waikato.ac.nz/~ml/weka/. Zadrozny, B. (2004). Learning and evaluating classifiers under sample selection bias. In Proceedings of the Twenty-first International Conference on Machine Learning, pp. 903-910. Zadrozny, B., & Elkan, C. (2001). Learning and making decisions when costs
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–The Expected Value Framework: Decomposing the Business Problem and Recomposing the Solution Pieces selection bias, A Brief Digression on Selection Bias–A Brief Digression on Selection Bias targeting best prospects with, Targeting the Best Prospects for a Charity Mailing–A Brief Digression on Selection Bias analytic skills, software skills vs., Implications for Managing the Data Science Team analytic
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to Data Mining Tasks Cray Computer Corporation, The Data credit-card transactions, Data Understanding, Profiling: Finding Typical Behavior creditworthiness model, as example of selection bias, A Brief Digression on Selection Bias CRISP cycle, Implications for Managing the Data Science Team approaches and, Implications for Managing the Data Science Team strategy and, Implications for Managing
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-neighbor reasoning, Dimensionality and domain knowledge–Dimensionality and domain knowledge directed marketing example, Targeting the Best Prospects for a Charity Mailing–A Brief Digression on Selection Bias discoveries, Data Science, Engineering, and Data-Driven Decision Making discrete (binary) classifiers, ROC Graphs and Curves discrete classifiers, ROC Graphs and Curves discretized numeric variables
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Viral Marketing Example detecting credit-card fraud, Profiling: Finding Typical Behavior directed marketing, Targeting the Best Prospects for a Charity Mailing–A Brief Digression on Selection Bias evaluating proposals, Scenario and Proposal–Flaws in the GGC Proposal evidence lift, Example: Evidence Lifts from Facebook “Likes”–Example: Evidence Lifts from Facebook “Likes” eWatch
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Predictive Modeling: From Correlation to Supervised Segmentation informative variables, Supervised Segmentation variables, Introduction to Predictive Modeling: From Correlation to Supervised Segmentation selection bias, A Brief Digression on Selection Bias–A Brief Digression on Selection Bias semantic similarity, syntactic vs., The news story clusters separating classes, Example: Overfitting Linear Functions sequential backward elimination, A General Method for
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Europe and, Privacy, Ethics, and Mining Data About Individuals targeting best prospects example, Targeting the Best Prospects for a Charity Mailing–A Brief Digression on Selection Bias tasks/techniques, Data Science, Engineering, and Data-Driven Decision Making, Other Data Science Tasks and Techniques–Summary associations, Co-occurrences and Associations: Finding Items That
Commodity Trading Advisors: Risk, Performance Analysis, and Selection
by Greg N. Gregoriou, Vassilios Karavas, François-Serge Lhabitant and Fabrice Douglas Rouah · 23 Sep 2004
an overall picture of CTA, as it is more representative of the performance of trading advisors as a whole and cannot be criticized as having selection bias. The sample portfolio is made up of CTA, Canadian, U.S., and international equities as well as domestic bonds. Canadian equities are represented by the
Advances in Financial Machine Learning
by Marcos Lopez de Prado · 2 Feb 2018 · 571pp · 105,054 words
-sample. 1.3.2.6 Overfitting Problem: Standard cross-validation methods fail in finance. Most discoveries in finance are false, due to multiple testing and selection bias. Solution: Whatever you do, always ask yourself in what way you may be overfitting. Be skeptical about your own work, and constantly challenge yourself to
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for CV's failure is that the testing set is used multiple times in the process of developing a model, leading to multiple testing and selection bias. We will revisit this second cause of failure in Chapters 11–13. For the time being, let us concern ourselves exclusively with the first cause
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, and hide the rest, your audience will not be able to deduce that these results are false positives, a statistical fluke. This phenomenon is called “selection bias.” Can you imagine one procedure to prevent this? What if we split the dataset in three sets: training, validation, and testing? The validation set is
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run only on the one configuration chosen in the validation phase. In what case does this procedure still fail? What is the key to avoiding selection bias? Bibliography Bharat Rao, R., G. Fung, and R. Rosales (2008): “On the dangers of cross-validation: An experimental evaluation.” White paper, IKM CKS Siemens Medical
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is like drinking and driving. Do not research under the influence of a backtest. Most backtests published in journals are flawed, as the result of selection bias on multiple tests (Bailey, Borwein, López de Prado, and Zhu [2014]; Harvey et al. [2016]). A full book could be written listing all the different
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sins of Luo et al. [2014] (there are more, but who's counting?). Professionals may produce flawless backtests, and will still fall for multiple testing, selection bias, or backtest overfitting (Bailey and López de Prado [2014b]). 11.4 Backtesting Is Not a Research Tool Chapter 8 discussed substitution effects, joint effects, masking
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a false discovery will drop substantially, but still they will not be zero. 11.5 A Few General Recommendations Backtest overfitting can be defined as selection bias on multiple backtests. Backtest overfitting takes place when a strategy is developed to perform well on a backtest, by monetizing random historical patterns. Because those
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unlikely to occur again in the future, the strategy so developed will fail. Every backtested strategy is overfit to some extent as a result of “selection bias”: The only backtests that most people share are those that portray supposedly winning investment strategies. How to address backtest overfitting is arguably the most fundamental
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. 15, No. 2 (Winter). Available at https://ssrn.com/abstract=1821643. Bailey, D. and M. López de Prado (2014b): “The deflated Sharpe ratio: Correcting for selection bias, backtest overfitting and non-normality.” Journal of Portfolio Management, Vol. 40, No. 5, pp. 94–107. Available at https://ssrn.com/abstract=2460551. Harvey, C
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CPCV's variance will be, and in the limit CPCV will report the true Sharpe ratio E[yi] with zero variance, . There will not be selection bias, because the strategy selected out of i = 1, …, I will be the one with the highest true Sharpe ratio. Of course, we know that zero
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ahead and present only the result with the higher Sharpe ratio, arguing that a strategy with a shorter warm-up is more realistic. Is this selection bias? Your strategy achieves a Sharpe ratio of 1.5 on a WF backtest, but a Sharpe ratio of 0.7 on a CV backtest. You
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higher Sharpe ratio, arguing that the WF backtest is historically accurate, while the CV backtest is a scenario simulation, or an inferential exercise. Is this selection bias? Your strategy produces 100,000 forecasts over time. You would like to derive the CPCV distribution of Sharpe ratios by generating 1,000 paths. What
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. 15, No. 2 (Winter). Available at https://ssrn.com/abstract=1821643. Bailey, D. and M. López de Prado (2014): “The deflated Sharpe ratio: Correcting for selection bias, backtest overfitting and non-normality.” Journal of Portfolio Management, Vol. 40, No. 5, pp. 94–107. Available at https://ssrn.com/abstract=2460551. Bailey, D
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on absolute or relative returns. Deflated Sharpe ratio: DSR corrects SR for inflationary effects caused by non-Normal returns, track record length, and multiple testing/selection bias. It should exceed 0.95, for the standard significance level of 5%. It can be computed on absolute or relative returns. 14.8 Classification Scores
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frontier.” Journal of Risk, Vol. 15, No. 2, pp. 3–44. Bailey, D. and M. López de Prado (2014): “The deflated Sharpe ratio: Correcting for selection bias, backtest overfitting and non-normality.” Journal of Portfolio Management, Vol. 40, No. 5. Available at https://ssrn.com/abstract=2460551. Barra (1998): Risk Model Handbook
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implied by Figure 13.5, for a weekly betting frequency? References Bailey, D. and M. López de Prado (2014): “The deflated Sharpe ratio: Correcting for selection bias, backtest overfitting and non-normality.” Journal of Portfolio Management, Vol. 40, No. 5. Available at https://ssrn.com/abstract=2460551. Bailey, D. and M. López
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us to pick a particular historical dataset where HRP outperforms CLA and IVP (see Bailey and López de Prado [2014], and recall our discussion of selection bias in Chapter 11). Instead, in this section we follow the backtesting paradigm explained in Chapter 13, and evaluate via Monte Carlo the performance out-of
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. 5, pp. 458–471. Available at http://ssrn.com/abstract=2308659. Bailey, D. and M. López de Prado (2014): “The deflated Sharpe ratio: Correcting for selection bias, backtest overfitting and non-normality.” Journal of Portfolio Management, Vol. 40, No. 5, pp. 94–107. Black, F. and R. Litterman (1992): “Global portfolio optimization
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LTAP as the baseline, the average reductions are only 0.164 kWh for both years. Part of the difference may be due to the self-selection bias in treatment groups, especially the active group, where the households have to explicitly opt-in to participate in the trial. It is likely that the
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are well-suited to take advantage of the proposed new pricing structure. We believe that the LTAP baseline is a way to address the self-selection bias and plan to conduct additional studies to further verify this. Figure 22.6 Gradient tree boosting (GBT) appears to follow recent usage too closely and
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tuning and need for skepticism optimal trading rule (OTR) framework for probability of. See Probability of backtest overfitting (PBO) random forest (RF) method to reduce selection bias and support vector machines (SVMs) and trading rules and walk-forward (WF) method and Backtest statistics classification measurements in drawdown (DD) and time under water
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redundancy and bagging classifiers in random forest (RF) overfitting and support vector machine (SVM) implementation in synthetic dataset generation in walk-forward timefolds method in Selection bias Sequential bootstraps description of implementation of leakage reduction using Monte Carlo experiments evaluating numerical example of Shannon, Claude Sharpe ratio (SR) in efficiency measurements annualized
Super Thinking: The Big Book of Mental Models
by Gabriel Weinberg and Lauren McCann · 17 Jun 2019
nonsmoking pregnant women start smoking. The smokers in the study would therefore be those who selected to continue smoking, which can introduce a bias called selection bias. With selection bias, there is no guarantee that the study has isolated smoking to be the only difference between these groups. So if there is a difference
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who choose to continue smoking during their pregnancy against the advice of doctors may similarly make other medically questionable choices, which could drive adverse outcomes. Selection bias can also occur when a sample is selected that is not representative of the broader population of interest, as with online reviews. If the group
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must be really careful when drawing conclusions based on nonrandom experiments. The Dilbert cartoon above pokes fun at the selection bias inherent in a lot of the studies reported in the news. A similar selection bias occurs with parents and school choice for their kids. Parents understandably want to give their kids a leg
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or because the students are better prepared due to their parents’ financial means and interest in education? Selection bias likely explains some significant portion of these schools’ better test scores and college admissions. Another type of selection bias, common to surveys, is nonresponse bias, which occurs when a subset of people don’t participate
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as one-to-ten ratings Respondents reporting things that reflect well on themselves It’s worth trying to account for all of these subtle biases (selection bias, nonresponse bias, response bias, survivorship bias), because after you do so, you can be even more sure of your conclusions. BE WARY OF THE “LAW
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can make you overlook possible suboptimal choices in a study’s design (e.g., sample size) or biases that could have crept in (e.g., selection bias). WILL IT REPLICATE? By now you should know that some experimental results are just flukes. In order to be sure a study result isn’t
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them. There are also many other reasons a study might not be replicable, including the various biases we’ve discussed in previous sections (e.g., selection bias, survivorship bias, etc.), which could have crept into the results. Another reason is that, by chance, the original study might have showcased a seemingly impressive
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within two. Any isolated experiment can result in a false positive or a false negative and can also be biased by myriad factors, most commonly selection bias, response bias, and survivorship bias. Replication increases confidence in results, so start by looking for a systematic review and/or meta-analysis when researching an
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, 289, 292 education and schools, 224–25, 241, 296 expectations and, 267–68 mindsets and, 267 school ranking, 137 school start times, 110, 111, 130 selection bias and, 140 textbooks in, 262 see also college effective altruism, 80 egalitarian versus hierarchical, in organizational culture, 274 80/20 arrangements, 80–81, 83 Einstein
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thermodynamics, 124 secrets, 288–90, 292 Securities and Exchange Commission, U.S., 228 security, false sense of, 44 security services, 229 selection, adverse, 46–47 selection bias, 139–40, 143, 170 self-control, 87 self-fulfilling prophecies, 267 self-serving bias, 21, 272 Seligman, Martin, 22 Semmelweis, Ignaz, 25–26 Semmelweis reflex
Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps
by Valliappa Lakshmanan, Sara Robinson and Michael Munn · 31 Oct 2020
of your model, and the chosen baseline. In this section, we’ll discuss some limitations of explainability, along with some alternatives to feature attributions. Data selection bias It’s often said that machine learning is “garbage in, garbage out.” In other words, a model is only as good as the data used
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representation of their training data. If we don’t catch data imbalances before training a model, explainability methods like feature attributions can help bring data selection bias to light. As an example, say we’re building a model to predict the type of boat present in an image. Let’s say it
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beam search algorithm, Problem Representation Design Patterns BERT, Context language models-Context language models, Embeddings of words versus sentences, Choosing a batch size biasdata, Data selection bias, Problem, Before training-Allow and disallow lists(see also data collection bias, data distribution bias, data representation bias, experimenter bias, implicit bias, problematic bias, proxy
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More Guns, Less Crime: Understanding Crime and Gun-Control Laws
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A Manual for Creating Atheists
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The Upside of Inequality
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Doing Harm: The Truth About How Bad Medicine and Lazy Science Leave Women Dismissed, Misdiagnosed, and Sick
by Maya Dusenbery · 6 Mar 2018 · 504pp · 147,722 words
All About Asset Allocation, Second Edition
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The Road to Somewhere: The Populist Revolt and the Future of Politics
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Data Action: Using Data for Public Good
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Outnumbered: From Facebook and Google to Fake News and Filter-Bubbles – the Algorithms That Control Our Lives
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Data Wrangling With Python: Tips and Tools to Make Your Life Easier
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The Hidden Half: How the World Conceals Its Secrets
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Everyday Utopia: What 2,000 Years of Wild Experiments Can Teach Us About the Good Life
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Wall Street: How It Works And for Whom
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SuperFreakonomics
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Investing Demystified: How to Invest Without Speculation and Sleepless Nights
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The Extended Phenotype: The Long Reach of the Gene
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The Book of Woe: The DSM and the Unmaking of Psychiatry
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After the Gig: How the Sharing Economy Got Hijacked and How to Win It Back
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The Alternative: How to Build a Just Economy
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Buffett
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Finance and the Good Society
by Robert J. Shiller · 1 Jan 2012 · 288pp · 16,556 words
In Pursuit of the Perfect Portfolio: The Stories, Voices, and Key Insights of the Pioneers Who Shaped the Way We Invest
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Seveneves
by Neal Stephenson · 19 May 2015 · 945pp · 292,893 words
Success and Luck: Good Fortune and the Myth of Meritocracy
by Robert H. Frank · 31 Mar 2016 · 190pp · 53,409 words
Animal: The Autobiography of a Female Body
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How Medicine Works and When It Doesn't: Learning Who to Trust to Get and Stay Healthy
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Practical Manual of Thyroid and Parathyroid Disease
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The Antidote: Happiness for People Who Can't Stand Positive Thinking
by Oliver Burkeman · 1 Jul 2012 · 211pp · 69,380 words
The Elusive Quest for Growth: Economists' Adventures and Misadventures in the Tropics
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Rewriting the Rules of the European Economy: An Agenda for Growth and Shared Prosperity
by Joseph E. Stiglitz · 28 Jan 2020 · 408pp · 108,985 words
Not Working: Where Have All the Good Jobs Gone?
by David G. Blanchflower · 12 Apr 2021 · 566pp · 160,453 words
The Internet Trap: How the Digital Economy Builds Monopolies and Undermines Democracy
by Matthew Hindman · 24 Sep 2018
The Rise and Decline of Nations: Economic Growth, Stagflation, and Social Rigidities
by Mancur Olson
The Age of the Infovore: Succeeding in the Information Economy
by Tyler Cowen · 25 May 2010 · 254pp · 72,929 words
The Despot's Accomplice: How the West Is Aiding and Abetting the Decline of Democracy
by Brian Klaas · 15 Mar 2017
Escape From Model Land: How Mathematical Models Can Lead Us Astray and What We Can Do About It
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The Shame Machine: Who Profits in the New Age of Humiliation
by Cathy O'Neil · 15 Mar 2022 · 318pp · 73,713 words
Empirical Market Microstructure: The Institutions, Economics and Econometrics of Securities Trading
by Joel Hasbrouck · 4 Jan 2007 · 209pp · 13,138 words
The Tyranny of Metrics
by Jerry Z. Muller · 23 Jan 2018 · 204pp · 53,261 words
DIY Investor: How to Take Control of Your Investments & Plan for a Financially Secure Future
by Andy Bell · 12 Sep 2013 · 348pp · 82,499 words
Aftermath: Following the Bloodshed of America's Wars in the Muslim World
by Nir Rosen · 21 Apr 2011 · 1,016pp · 283,960 words
Tools of Titans: The Tactics, Routines, and Habits of Billionaires, Icons, and World-Class Performers
by Timothy Ferriss · 6 Dec 2016 · 669pp · 210,153 words
The Singularity Is Near: When Humans Transcend Biology
by Ray Kurzweil · 14 Jul 2005 · 761pp · 231,902 words
Urban Transport Without the Hot Air, Volume 1
by Steve Melia · 351pp · 91,133 words
Getting Back to Full Employment: A Better Bargain for Working People
by Dean Baker and Jared Bernstein · 14 Nov 2013 · 128pp · 35,958 words
A Demon of Our Own Design: Markets, Hedge Funds, and the Perils of Financial Innovation
by Richard Bookstaber · 5 Apr 2007 · 289pp · 113,211 words
Exceptional People: How Migration Shaped Our World and Will Define Our Future
by Ian Goldin, Geoffrey Cameron and Meera Balarajan · 20 Dec 2010 · 482pp · 117,962 words
Miracle Cure
by William Rosen · 14 Apr 2017 · 515pp · 117,501 words
Circus Maximus: The Economic Gamble Behind Hosting the Olympics and the World Cup
by Andrew Zimbalist · 13 Jan 2015 · 222pp · 60,207 words
In the Plex: How Google Thinks, Works, and Shapes Our Lives
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Laziness Does Not Exist
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The Authoritarians
by Robert Altemeyer · 2 Jan 2007 · 298pp · 87,023 words
The Case Against Sugar
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Destined for War: America, China, and Thucydides's Trap
by Graham Allison · 29 May 2017 · 518pp · 128,324 words
Anathem
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The Lies That Bind: Rethinking Identity
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Economic Gangsters: Corruption, Violence, and the Poverty of Nations
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Autistic Community and the Neurodiversity Movement: Stories From the Frontline
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Battling Eight Giants: Basic Income Now
by Guy Standing · 19 Mar 2020
Psychopathy: An Introduction to Biological Findings and Their Implications
by Andrea L. Glenn and Adrian Raine · 7 Mar 2014
Risk Management in Trading
by Davis Edwards · 10 Jul 2014
Culture & Empire: Digital Revolution
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Going Solo: The Extraordinary Rise and Surprising Appeal of Living Alone
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Reinventing Organizations: A Guide to Creating Organizations Inspired by the Next Stage of Human Consciousness
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Mastering the VC Game: A Venture Capital Insider Reveals How to Get From Start-Up to IPO on Your Terms
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Heart: A History
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Stress Test: Reflections on Financial Crises
by Timothy F. Geithner · 11 May 2014 · 593pp · 189,857 words
Robots Will Steal Your Job, But That's OK: How to Survive the Economic Collapse and Be Happy
by Pistono, Federico · 14 Oct 2012 · 245pp · 64,288 words
Producing Open Source Software: How to Run a Successful Free Software Project
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In Pursuit of Privilege: A History of New York City's Upper Class and the Making of a Metropolis
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The Idealist: Aaron Swartz and the Rise of Free Culture on the Internet
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Different: Escaping the Competitive Herd
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Swimming With Sharks: My Journey into the World of the Bankers
by Joris Luyendijk · 14 Sep 2015 · 257pp · 71,686 words
The Sum of Small Things: A Theory of the Aspirational Class
by Elizabeth Currid-Halkett · 14 May 2017 · 550pp · 89,316 words
When Einstein Walked With Gödel: Excursions to the Edge of Thought
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Five Billion Years of Solitude: The Search for Life Among the Stars
by Lee Billings · 2 Oct 2013 · 326pp · 97,089 words
Reinventing Discovery: The New Era of Networked Science
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Reentry: SpaceX, Elon Musk, and the Reusable Rockets That Launched a Second Space Age
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The Purpose Economy: How Your Desire for Impact, Personal Growth and Community Is Changing the World
by Aaron Hurst · 31 Aug 2013 · 209pp · 63,649 words
The Lost Decade: 2010–2020, and What Lies Ahead for Britain
by Polly Toynbee and David Walker · 3 Mar 2020 · 279pp · 90,888 words
Energy: A Human History
by Richard Rhodes · 28 May 2018 · 653pp · 155,847 words
Young Money: Inside the Hidden World of Wall Street's Post-Crash Recruits
by Kevin Roose · 18 Feb 2014 · 269pp · 83,307 words
The Lean Startup: How Today’s Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses
by Eric Ries · 13 Sep 2011 · 278pp · 83,468 words
4th Rock From the Sun: The Story of Mars
by Nicky Jenner · 5 Apr 2017 · 294pp · 87,986 words
Fed Up: An Insider's Take on Why the Federal Reserve Is Bad for America
by Danielle Dimartino Booth · 14 Feb 2017 · 479pp · 113,510 words
Quantum Computing for Everyone
by Chris Bernhardt · 19 Mar 2019 · 211pp · 57,618 words