SSRN Viewer

With the advent in recent years of large financial data sets, machine learning and high-performance computing, analysts can backtest millions (if not billions) of alternative investment strategies. Backtest optimizers search for combinations of parameters that maximize the simulated historical performance of a strategy, leading to backtest overfitting. The problem of performance inflation extends beyond backtesting. More generally, researchers and investors tend to report only positive outcomes, a phenomenon known as selection bias. Not controlling for the number of trials involved in a particular discovery leads to over-optimistic performance expectations. The Deflated Sharpe Ratio (DSR) corrects for two leading sources of performance inflation: Selection bias under multiple testing and non-Normally distributed returns. In doing so, DSR helps separate legitimate empirical findings from statistical flukes.

High-frequency trading has become a dominant force in the U.S. capital market, accounting for over 70% of dollar trading volume. This study examines the implication of high-frequency trading for stock price volatility and price discovery. I find that high-frequency trading is positively correlated with stock price volatility after controlling for firm fundamental volatility and other exogenous determinants of volatility. The positive correlation is stronger among the top 3,000 stocks in market capitalization and among stocks with high institutional holdings. The positive correlation is also stronger during periods of high market uncertainty. Furthermore, I find that high-frequency trading is negatively related to the market’s ability to incorporate information about firm fundamentals into asset prices. Stock prices tend to overreact to fundamental news when high-frequency trading is at a high volume. Overall, this paper demonstrates that high-frequency trading may potentially have some harmful effects for the U.S. capital market.

Bitcoin (BTC) returns exhibit pronounced positive skewness with a third central moment of approximately 150% per year. They are well characterized by a mixture of Normals distribution with one “normal” regime and a small probability of a “bliss” regime where the price appreciation is more than 100 times at the annual horizon. The large right-tail skew induces investors with preferences for positive skewness to add significant BTC holdings to equity-bond portfolios. Even when BTC is forecast to lose half of its value in the normal regime, investors with power utility optimally add 3% allocations to BTC when the probability of the bliss regime is around 1%. Cumulative Prospect Theory investors are even more sensitive to positive skewness and hold BTC allocations of around 3% when the probability of the bliss regime is 0.0006 and the mean of BTC in the normal regime corresponds to a loss of 90%.

The recent financial crisis has led to a major debate about fair-value accounting. Many critics have argued that fair-value accounting, often also called mark-to-market accounting, has significantly contributed to the financial crisis or, at least, exacerbated its severity. In this paper, we assess these arguments and examine the role of fair-value accounting in the financial crisis using descriptive data and empirical evidence. Based on our analysis, it is unlikely that fair-value accounting added to the severity of the 2008 financial crisis in a major way. While there may have been downward spirals or asset-fire sales in certain markets, we find little evidence that these effects are the result of fair-value accounting. We also find little support for claims that fair-value accounting leads to excessive writedowns of banks’ assets. If anything, empirical evidence to date points in the opposite direction, that is, towards overvaluation of bank assets.

Investor behaviour often deviates from logic and reason, and investors display many behaviour biases that influence their investment decision-making processes. The authors describe some common behavioural biases and suggest how to mitigate them.

The consensus view of the influential economist John Maynard Keynes is that he was a stellar investor. We provide an extensive quantitative appraisal of his performance over a quarter-century in both calendar and event time, and present detailed empirical analysis of his archived trading records. His top-down approach generated disappointing returns in the 1920s and we find no evidence of any market-timing ability. However, from the early 1930s his performance improved as he evolved into a bottom-up stock-picker with high tracking error, substantial active risk, and pronounced size and value tilts. Our careful reconstruction of Keynes’ stock trading provides a unique record of realized performance and sheds light on how equity focussed investing developed historically.

We review the main processes used to model financial variables. We emphasize the parallel between discrete-time processes, mainly used by econometricians for risk- and portfolio-management, and their continuous-time counterparts, mainly used by mathematicians to price derivatives. We highlight the relationship of such processes with the building blocks of stochastic dynamics and statistical inference, namely the invariants. Figures and practical examples support intuition. Fully documented code illustrating these processes in practice is available for download

This survey introduces and reviews the field of behavioral finance. It outlines the traditional finance approach, which builds upon rational acting investors, its assumptions, and its shortcomings. Moreover, it surveys the main findings from psychology and sociology that contrast with this traditional finance approach, and it provides examples of situations and studies that reveal the relevance of these findings for financial markets and its participants.

The anomalous returns associated with net stock issues, accruals, and momentum are pervasive; they show up in all size groups (micro, small, and big) in cross-section regressions, and they are also strong in sorts, at least in the extremes. The asset growth and profitability anomalies are less robust. There is an asset growth anomaly in average returns on microcaps and small stocks, but it is absent for big stocks. Among profitable firms, higher profitability tends to be associated with abnormally high returns, but there is little evidence that unprofitable firms have unusually low returns.

We study whether carbon emissions affect the cross-section of U.S. stock returns. We find that stocks of firms with higher total CO2 emissions (and changes in emissions) earn higher returns, controlling for size, book-to-market, and other return predictors. We cannot explain this carbon premium through differences in unexpected profitability or other known risk factors. We also find that institutional investors implement exclusionary screening based on direct emission intensity (the ratio of total emissions to sales) in a few salient industries. Overall, our results are consistent with an interpretation that investors are already demanding compensation for their exposure to carbon emission risk.

Using ESG ratings from seven different data providers for a sample of S&P 500 firms between 2010 and 2017, we study the relation between ESG rating disagreement and stock returns. We find that stock returns are positively related to ESG rating disagreement, suggesting a risk premium for firms with higher ESG rating disagreement. The relation is primarily driven by disagreement about the environmental dimension. We discuss the practical implications of our findings for firms’ equity cost of capital as well as for investment managers and asset owners who use ESG investment strategies.

This article studies the relative investment performance of several stock-valuation measures. The first is mispricing based on the valuation model developed by Bakshe and Chen (1998)and extended by Dong (1998) (hereafter, the BCD model). The BCD model relates, in closed form, a stock's fair value to (i) the firm's net earnings per share (EPS). (ii) the expected future EPS growth and (iii) long-term rate. The second is a value/ price (V/P) ratio based on the Lee-Myers-Swaminathan (1999) residual-income model. The other measures are all indirect valuation indicators, including book/market (B/M), earnings/price (E/P), size, and past return momentum. These measures are shown to possess distinct properties. For example, the B/M, E/P and V/P ratios are highly persistent over time: high (low) B/M stocks tend to maintain high (low) B/M ratios. But, the BCD model mispricing is highly mean-reverting: an overpriced group will eventually become underpriced (in about 1.5 years on average), and vice versa. More importantly, the BCD model mispricing, momentum, size V/P and B/M are, in decreasing order, significant ex ante predictors of future returns. The best investment strategy is to combine the BCD model mispricing with momentum rankings. Indeed, if one would hold an equally-weighted portfolio of stocks that are the most underpriced and that have top momentum, the average monthly return from 1979 to 1996 would have been 3.18 percent, with a monthly Jensen's alpha of about 1.5 percent.

This paper yields a summary score that informs about the sustainability (or persistence) of earnings and about the trailing P/E ratio. The score is delivered from a model that identifies unsustainable earnings from the financial statements by exploiting accounting relations that require that unsustainable earnings leave a trail in the accounts. The paper also builds a P/E model that recognizes that investors buy future earnings, so should pay less for current earnings if those earnings cannot be sustained in the future. In out-of-sample prediction tests, the analysis reliably identifies unsustainable earnings, and also explains cross-sectional differences in P/E ratios. The paper also finds that stock returns are predictable when traded P/E ratios differ from those indicated by our P/E model.

In this paper we provide a mathematical derivation that links traditional time-value-of-money concepts to Metcalfe value, and use Bitcoin, Facebook as numerical examples of the proof. There is compelling evidence that suggests that the growth and price of bitcoin and other cryptocurrencies are likely to proceed according to a relatively straightforward mathematical model similar to the growth curves of Facebook and other networks. Using observed data for Bitcoin, we derive the relationships between price, number of users, and time, and show that the resulting market prices likely follow a Gompertz sigmoid growth function. This function, historically used to describe the growth of biological organisms like bacteria, tumors, and viruses, likely has some application to network economics. We conclude that the long-term growth rate in users has considerable effect on the long-term price of bitcoin.

The purpose of this paper is firstly to review the literature on the efficacy and importance of the Altman Z-Score bankruptcy prediction model globally and its applications in finance and related areas. This review is based on an analysis of 33 scientific papers published from the year 2000 in leading financial and accounting journals. Secondly, we use a large international sample of firms to assess the classification performance of the model in bankruptcy and distressed firm prediction. In all, we analyze its performance on firms from 31 European and three non-European countries. This kind of comprehensive international analysis has not been presented thus far. Except for the U.S. and China, the firms in the sample are primarily private and cover non-financial companies across all industrial sectors. Thus, the version of the Z-Score model developed by Altman (1983) for private manufacturing and non-manufacturing firms (Z"-Score Model) is used in our testing. The literature review shows that results for Z-Score Models have been somewhat uneven in that in some studies the model has performed very well, whereas in others it has been outperformed by competing models. None of the reviewed studies is based on a comprehensive international comparison, which makes the results difficult to generalize. The analysis in this study shows that while a general international model works reasonably well, for most countries, with prediction accuracy levels (AUC) of about 75%, and exceptionally well for some (above 90%), the classification accuracy may be considerably improved with country-specific estimation especially with the use of additional variables. In some country models, the information provided by additional variables helps boost the classification accuracy to a higher level.

Numerous academic studies have shown that asset allocation is the single most important determinant of portfolio returns. We accept this premise but note that an optimal asset allocation strategy must still be determined based on dynamic conditions. Using the principles of intermarket analysis and the relationship between the total return of the 10-year Treasury and the 30-year Treasury, we develop one such strategy. We find that an active strategy that uses the signaling power of these Treasury bonds to position into either the stock market or Treasuries can be used to outperform a buy and hold stock portfolio on an absolute and risk-adjusted basis. We also find that the signaling power of Treasuries can be used to enhance asset allocation decisions and traditional rebalancing. The predictive behavior of Treasuries on equities is a market anomaly that has persisted over time, and has served as an anticipatory gauge of expansionary or contractionary conditions which favor stocks or bonds. Contrary to the Efficient Market Hypothesis, the information provided by relative total return Treasury movement does not appear to be priced in immediately by broad stock market averages, and therefore may be exploitable for active traders and tactical asset allocators.

We develop a method that identifies the attention paid by earnings call participants to firms' climate change exposures. The method adapts a machine learning keyword discovery algorithm and captures exposures related to opportunity, physical, and regulatory shocks associated with climate change. The measures are available for more than 10,000 firms from 34 countries between 2002 and 2020. We show that the measures are useful in predicting important real outcomes related to the net-zero transition, in particular, job creation in disruptive green technologies and green patenting, and that they contain information that is priced in options and equity markets.

In this paper, we document that an application of a moving average strategy of technical analysis to portfolios sorted by volatility generates investment timing portfolios that often outperform the buy-and-hold strategy substantially. For high volatility portfolios, the abnormal returns, relative to the CAPM and the Fama-French three-factor models, are high, and higher than those from the well known momentum strategy. The abnormal returns remain high even after accounting for transaction costs. Although both the moving average and the momentum strategies are trend-following methods, their performances are surprisingly uncorrelated and behave differently over the business cycles, default and liquidity risk.

We use deep neural networks to estimate an asset pricing model for individual stock returns that takes advantage of the vast amount of conditioning information, while keeping a fully flexible form and accounting for time-variation. The key innovations are to use the fundamental no-arbitrage condition as criterion function, to construct the most informative test assets with an adversarial approach and to extract the states of the economy from many macroeconomic time series. Our asset pricing model outperforms out-of-sample all benchmark approaches in terms of Sharpe ratio, explained variation and pricing errors and identifies the key factors that drive asset prices.

Based on high frequency data of the S&P 500 ETF from 1993–2013, we document an intraday momentum pattern: the first half-hour return on the market since the previous day’s market close predicts the last half-hour return. The predictability, both statistically and economically significant, is stronger on more volatile days, on higher volume days, on recession days, and on major macroeconomic news release days. This intraday momentum is also present for ten other most actively traded domestic and international ETFs. Theoretically, the intraday momentum is consistent not only with Bogousslavsky’s (2016) model of portfolio infrequent rebalancing, but also with a model of trading on late-informed news near the market close.

SUBJECT AREAS: Corporate Finance, Valuation, Capital Budgeting, Investment Policy, Economic Value Added, EVA, Market Value Added, MVA, Net Present Value, NPV, cash flows, free cash flows real free cash flows This technical note studies Economic Value Added, EVA. First, a conceptual framework regarding Net Present Value, NPV, is presented. Second, the note presents an approach for calculating the free cash flow of a project starting from the periodic net cash flows. Third, a procedure for calculating the real free cash flow is developed. Fourth, the EVA is presented and contrasted to NPV. EVA starts from accounting figures (profit) and NPV starts from net cash flows. The coincidence between MVA and NPV is examined. Four examples are presented which reveal some inconsistencies between the two measurement. The four examples show circumstances where EVA underestimates the value generated by a project or firm as compared with the NPV. An approach for calculating the real EVA is presented. The note offers reflections upon figures that should be employed in order to calculate the cost of the invested capital or equity to be included in EVA calculation. Finally, different approaches to calculate EVA and MVA are compared with NPV results.

edging short gamma exposure requires trading in the direction of price movements,thereby creating price momentum. Using intraday returns on over 60 futures on equities,bonds, commodities, and currencies between 1974 and 2020, we document strong “marketintraday momentum” everywhere. The return during the last 30 minutes before the marketclose is positively predicted by the return during the rest of the day (from previous marketclose to the last 30 minutes). The predictive power is economically and statistically highlysignificant, and reverts over the next days. We provide novel evidence that links marketintraday momentum to the gamma hedging demand from market participants such as marketmakers of options and leveraged ETFs.

We find that the stocks in which active mutual fund or hedge fund managers display the most conviction towards ex-ante, their “Best ideas,” outperform the market, as well as the other stocks in those managers' portfolios, by approximately 2.8 to 4.5 percent per year, depending on the benchmark employed. The vast majority of the other stocks managers hold do not exhibit significant outperformance. Thus, the organization of the money management industry appears to make it optimal for managers to introduce stocks into their portfolio that are not outperformers. We argue that investors would benefit if managers held more concentrated portfolios.

Using 1.7 trillion dollars of live trade execution data from a large institutional money manager across 21 developed equity markets over a 19-year period, we measure the real-world trading costs and price impact function of a large trader. We provide a novel description of how costs vary across trade type, stock characteristics, trade size, time, and exchanges globally to test various theories of price impact. We find actual trading costs to be an order of magnitude smaller than previous studies suggest, and describe the trading process leading to these costs. A model calibrated to match the distribution of actual costs across trade size, stocks, and time outperforms other models from the literature in out of sample tests that attempt to describe independent costs from brokers and realized costs of traded index funds. Our model based on realized costs from live trades portrays very different implementation costs than previous studies suggest.

VAA (Vigilant Asset Allocation) is a dual-momentum based investment strategy with a vigorous crash protection and a fast momentum filter. Dual momentum combines absolute (trendfollowing) and relative (strength) momentum. Compared to the traditional dual momentum approaches, we have replaced the usual crash protection through trendfollowing on the asset level by our breadth momentum on the universe level instead. As a result, the VAA strategy is on average often more than 50% out of the market. We show, however, that the resulting momentum strategy is by no means sluggish. By using large and small universes with US and global ETF-like monthly data starting 1925 and 1969 respectively, we arrive out-of-sample at annual returns above 10% with max drawdowns below 15% for each of these four universes.