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We contrast the time-series and cross-sectional performance of three popular investment strategies: carry, momentum and value. While considerable research has examined the performance of these strategies in either a directional or cross-asset settings, we offer some insights on the market conditions that favor the application of a particular setting.

I review empirical research on the relation between capital markets and financial statements. The principal sources of demand for capital markets research in accounting are fundamental analysis and valuation, tests of market efficiency, and the role of accounting numbers in contracts and the political process. The capital markets research topics of current interest to researchers include tests of market efficiency with respect to accounting information, fundamental analysis, and value relevance of financial reporting. Evidence from research on these topics is likely to be helpful in capital market investment decisions, accounting standard setting, and corporate financial disclosure decisions.

It is widely believed that the long-standing and highly popular value investing strategy—investing in low-valued stocks and selling short high-valued equities—lost its edge in the past 10-12 years. The reasons for this putative failure of value investing elude investors and academics, making it a challenge to assess the likelihood of the return of value investing to its days of glory. Based on extensive data analysis we show that value investing has generally been unprofitable for almost 30 years, barring a brief resurrection following the dotcom bust. We identify two major reasons for the failure of value investing: (1) accounting deficiencies causing systematic misidentification of value, and particularly of glamour (growth) stocks, and (2) fundamental economic developments which slowed down significantly the reshuffling of value and glamour stocks (mean reversion) which drove the erstwhile gains from the value strategy. We end up by identifying the type of companies (stocks) that may still generate gains from value investing.

We introduce a new approach to algorithmic investment management that yields profitable automated trading strategies. This trading model design is the result of a path of investigation that was chosen nearly three decades ago. Back then, a paradigm change was proposed for the way time is defined in financial markets, based on intrinsic events. This definition lead to the uncovering of a large set of scaling laws. An additional guiding principle was found by embedding the trading model construction in an agent-base framework, inspired by the study of complex systems. This new approach to designing automated trading algorithms is a parsimonious method for building a new type of investment strategy that not only generates profits, but also provides liquidity to financial markets and does not have a priori restrictions on the amount of assets that are managed.

We examine whether application of International Accounting Standards is associated with higher accounting quality. The application of IAS reflects the combined effects of features of the financial reporting system, including standards, their interpretation, enforcement, and litigation. We find that firms applying IAS from 21 countries generally evidence less earnings management, more timely loss recognition, and more value relevance of accounting amounts than do a matched sample of firms applying non-US domestic standards. Differences in accounting quality between the two groups of firms in the period before the IAS firms adopt IAS do not account for the post-adoption differences. We also find that firms applying IAS generally evidence an improvement in accounting quality between the pre- and post-adoption periods. Although we cannot be sure that our findings are attributable to the change in the financial reporting system rather than to changes in firms' incentives and the economic environment, we include research design features to mitigate the effects of both.

These are my Lecture Notes for a course in Continuous Time Finance which I taught in the Summer term 2003 at the University of Kaiserslautern. I am aware that the notes are not yet free of error and the manuscrip needs further improvement. I am happy about any comment on the notes. Please send your comments via e-mail to ce16@standrews.ac.uk.

High-frequency trading (HFT) has recently drawn massive public attention fuelled by the U.S. May 6, 2010 flash crash and the tremendous increases in trading volumes of HFT strategies. Indisputably, HFT is an important factor in markets that are driven by sophisticated technology on all layers of the trading value chain. However, discussions on this topic often lack sufficient and precise information. A remarkable gap between the results of academic research on HFT and its perceived impact on markets in the public, media and regulatory discussions can be observed. The research at hand aims to provide up-to-date background information on HFT. This includes definitions, drivers, strategies, academic research and current regulatory discussions. It analyzes HFT and thus contributes to the ongoing discussions by evaluating certain proposed regulatory measures, trying to offer new perspectives and deliver solution proposals.

One of the most influential ideas in the past 30 years is the Efficient Markets Hypothesis, the idea that market prices incorporate all information rationally and instantaneously. However, the emerging discipline of behavioral economics and finance has challenged this hypothesis, arguing that markets are not rational, but are driven by fear and greed instead. Recent research in the cognitive neurosciences suggests that these two perspectives are opposite sides of the same coin. In this article I propose a new framework that reconciles market efficiency with behavioral alternatives by applying the principles of evolution - competition, adaptation, and natural selection - to financial interactions. By extending Herbert Simon's notion of "satisficing" with evolutionary dynamics, I argue that much of what behavioralists cite as counterexamples to economic rationality - loss aversion, overconfidence, overreaction, mental accounting, and other behavioral biases - are, in fact, consistent with an evolutionary model of individuals adapting to a changing environment via simple heuristics. Despite the qualitative nature of this new paradigm, the Adaptive Markets Hypothesis offers a number of surprisingly concrete implications for the practice of portfolio management.

We document strong persistence in the performance of trades of individual investors. The correlation of the risk-adjusted performance of an individual across sample periods is about 10 percent. Investors classified in the top performance decile in the first half of our sample subsequently outperform those in the bottom decile by about 8 percent per year. Strategies long in firms purchased by previously successful investors and short in firms purchased by previously unsuccessful investors earn abnormal returns of 5 basis points per day. These returns are not confined to small stocks nor to stocks in which the investors are likely to have inside information. Our results suggest that skillful individual investors exploit market inefficiencies to earn abnormal profits, above and beyond any profits available from well-known strategies based upon size, value, or momentum. Presentation slides are available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3217430

The anomalies literature is infested with widespread p-hacking. We replicate this literature by compiling a large data library with 447 anomalies. With microcaps alleviated via NYSE breakpoints and value-weighted returns, 286 anomalies (64%) including 95 out of 102 liquidity variables (93%) are insignificant at the 5% level. Imposing the t-cutoff of three raises the number of insignificance to 380 (85%). Even for the 161 significant anomalies, their magnitudes are often much lower than originally reported. Among the 161, the q-factor model leaves 115 alphas insignificant (150 with t < 3). In all, capital markets are more efficient than previously recognized.

Digital currencies are a globally spreading phenomenon that is frequently and also prominently addressed by media, venture capitalists, financial and governmental institutions alike. As exchange prices for Bitcoin have reached multiple peaks within 2013, we pose a prevailing and yet academically unaddressed question: What are users' intentions when changing their domestic into a digital currency? In particular, this paper aims at giving empirical insights on whether users’ interest regarding digital currencies is driven by its appeal as an asset or as a currency. Based on our evaluation, we find strong indications that especially uninformed users approaching digital currencies are not primarily interested in an alternative transaction system but seek to participate in an alternative investment vehicle.

This paper analyzes the relationship between employee satisfaction and long-run stock returns. A value-weighted portfolio of the "100 Best Companies to Work For in America" earned an annual four-factor alpha of 3.5% from 1984-2009, and 2.1% above industry benchmarks. The results are robust to controls for firm characteristics, different weighting methodologies and the removal of outliers. The Best Companies also exhibited significantly more positive earnings surprises and announcement returns. These findings have three main implications. First, consistent with human capital-centered theories of the firm, employee satisfaction is positively correlated with shareholder returns and need not represent managerial slack. Second, the stock market does not fully value intangibles, even when independently verified by a highly public survey on large firms. Third, certain socially responsible investing ("SRI") screens may improve investment returns.

I define the micro-price to be the limit of a sequence of expected mid-prices and provide conditions for this limit to exist. The micro-price is a martingale by construction and can be considered to be the ‘fair’ price of an asset, conditional on the information in the order book. The micro-price may be expressed as an adjustment to the mid-price that takes into account the bid-ask spread and the imbalance. The micro-price can be estimated using high frequency data. I show empirically that it is a better predictor of short term prices than the mid-price or the weighted mid-price.Get the code and sample data on Github: https://github.com/sstoikov/micropriceWatch the video intro on YouTube: https://www.youtube.com/watch?v=0ZHypIAxYNo

A new measure of hedging pressure in commodity options markets—commercial hedgers’ net short option exposure—predicts option returns and changes in the slope of implied volatility curves. Puts are more expensive, and calls are cheaper, when values of option hedging pressure are greater. This pattern is consistent with commercial traders’ natural hedging motives. A strategy that provides liquidity to hedgers earns an average excess return of 6.4% per month before transaction costs and consideration of margin requirements. Overall, our results confirm the existence of hedging premiums, demand effects, and limits to arbitrage in commodity option markets.

The battle between proponents of the Efficient Markets Hypothesis and champions of behavioral finance has never been more pitched, and there is little consensus as to which side is winning or what the implications are for investment management and consulting. In this article, I review the case for and against the Efficient Markets Hypothesis, and describe a new framework - the Adaptive Markets Hypothesis - in which the traditional models of modern financial economics can co-exist alongside behavioral models in an intellectually consistent manner. Based on evolutionary principles, the Adaptive Markets Hypothesis implies that the degree of market efficiency is related to environmental factors characterizing market ecology such as the number of competitors in the market, the magnitude of profit opportunities available, and the adaptability of the market participants. Many of the examples that behavioralists cite as violations of rationality that are inconsistent with market efficiency - loss aversion, overconfidence, overreaction, mental accounting, and other behavioral biases - are, in fact, consistent with an evolutionary model of individuals adapting to a changing environment via simple heuristics. Despite the qualitative nature of this new paradigm, I show that the Adaptive Markets Hypothesis yields a number of surprisingly concrete applications for both investment managers and consultants.

We provide data and code that successfully reproduces nearly all cross-sectional stock return predictors. Our 319 characteristics draw from previous meta-studies, but we differ by comparing our t-stats to the original papers' results. For the 161 characteristics that were clearly significant in the original papers, 98% of our long-short portfolios find t-stats above 1.96. For the 44 characteristics that had mixed evidence, our reproductions find t-stats of 2 on average. A regression of reproduced t-stats on original long-short t-stats finds a slope of 0.88 and an R^2 of 82%. Mean returns are monotonic in predictive signals at the characteristic level. The remaining 114 characteristics were insignificant in the original papers or are modifications of the originals created by Hou, Xue, and Zhang (2020). These remaining characteristics are almost always significant if the original characteristic was also significant.

La versión española de este artículo se puede encontrar en: http://ssrn.com/abstract=1159045.In this paper, we analyze frequently made errors when valuing real options. The best way of doing it is through examples. We start by analyzing Damodaran proposal to value the option to expand the business of Home Depot. Some of the errors and problems of this and other approaches are:- Assuming that the option is replicable and using Black and Scholes' formula.- The estimation of the option's volatility is arbitrary and has a decisive effect on the option's value.- As there is no riskless arbitrage, the value of the option to expand basically depends on expectations about future cash flows. However, Damodaran assumes that this parameter does not influence the option's value (he does not use it) because he assumes that the option is replicable.- It is not appropriate to discount the expected value of the cash flows at the risk-free rate (as is done implicitly when Black and Scholes' formula is used) because the uncertainty of costs and sales in the exercise date may be greater or less than that estimated today.- Damodaran's valuation assumes that we know exactly the exercise price.- Belief that options' value increases when interest rates increase.- "Play" with volatility.- Valuing contracts as real options when they are not.

Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. In this course, we discuss scientifically sound ML tools that have been successfully applied to the management of large pools of funds.

Using a unique dataset of private equity funds over the last two decades, this paper analyzes the cash flow, return, and risk characteristics of private equity. Unlike previous studies, we have detailed cash flow data for each fund, rather than aggregate or accounting returns. We also know the exact timing of investments and capital returns to investors and the number and types of companies each fund invested in. We document the draw down and capital return schedules for the typical private equity fund, and show that it takes several years for capital to be invested, and over ten years for capital to be returned to generate excess returns. We provide several determining factors for these schedules, including existing investment opportunities and competition amongst private equity funds. In terms of performance, we document that private equity generates excess returns on the order of five to eight percent per annum relative to the aggregate public equity market. Moreover, while we estimate the betas of the private equity funds' portfolios to be greater than one, we show that on a risk-adjusted basis the excess value of the typical private equity fund is on the order of 24 percent relative to the present value of the invested capital. One interpretation of this magnitude is that it represents compensation for holding a 10-year illiquid investment.

We survey recent methodological contributions in asset pricing using factor models and machine learning. We organize these results based on their primary objectives: estimating expected returns, factors, risk exposures, risk premia, and the stochastic discount factor, as well as model comparison and alpha testing. We also discuss a variety of asymptotic schemes for inference. Our survey is a guide for financial economists interested in harnessing modern tools with rigor, robustness, and power to make new asset pricing discoveries, and it highlights directions for future research and methodological advances.

We show that there is remarkably low correlation (only 0.09) among different proposed proxies for investor disagreement, such as analysts’ forecast dispersion, short trading volume, and idiosyncratic volatility. This disagreement of the disagreement proxies makes it challenging to assess the impact on prices given results will depend on the choice of proxy. We develop a new unified framework to establish expected relationships among disparate measures of disagreement. Our model reveals a novel nonlinear composite measure, which is more predictive of returns than extant measures. A decile spread portfolio sorted on our composite measure generates a value-weighted alpha of 15.2% per year. Our model also provides a disagreement-based rationale for the predictive strength of idiosyncratic volatility, which is absorbed by our composite disagreement in empirical tests.Updates from November 28, 2023 version: (1) We add a unitless short interest-based disagreement measure; (2) We now use unitless measures for all composite disagreement inputs (including unadjusted IVOL and forecast dispersion DISP) so that our composite score is also unitless and therefore more suitable for cross sectional comparisons.Future versions: (1) Independently replicate iSKEW measure; (2) Update data.

Many acquisitions and some large strategic investments are often justified with the argument that they will create synergy. In this paper, we consider the various sources of synergy and categorize them into operating and financial synergies. We then examine how best to value synergy in any investment and how sensitive this value is to different assumptions. We also look at how this synergy value should be divided between the parties (or companies) involved in the investment. We conclude with an empirical examination of how much synergy is actually created in corporate mergers, and how much is paid. Synergy, we conclude, is so seldom delivered in acquisitions because it is incorrectly valued, inadequately planned for and much more difficult to create in practice than it is to compute on paper.

When prices reflect all available information, they oscillate around an equilibrium level. This oscillation is the result of the temporary market impact caused by waves of buyers and sellers. This price behavior can be approximated through an Ornstein-Uhlenbeck (OU) process.Market makers provide liquidity in an attempt to monetize this oscillation. They enter a long position when a security is priced below its estimated equilibrium level, and they enter a short position when a security is priced above its estimated equilibrium level. They hold that position until one of three outcomes occur: (1) they achieve the targeted profit; (2) they experience a maximum tolerated loss; (3) the position is held beyond a maximum tolerated horizon.All market makers are confronted with the problem of defining profit-taking and stop-out levels. More generally, all execution traders holding a particular position for a client must determine at what levels an order must be fulfilled. Those optimal levels can be determined by maximizing the trader's Sharpe ratio in the context of OU processes via Monte Carlo experiments. This paper develops an analytical framework and derives those optimal levels by using the method of heat potentials.

In sharp contrast to the recommendations of Modern Portfolio Theory (MPT), a vast majority of investors are not well diversified. This neglect of diversification is seen across all wealth segments, including the affluent. This paper attempts to provide a solution to this "diversification paradox," by expanding the Markowitz Framework of diversifying market risk to also include the concepts of Personal Risk and Aspirational Goals. The Wealth Allocation Framework enables individual investors to construct appropriate portfolios using all their assets, such as their home, mortgage, market investments and human capital. The investor may choose to accept a slightly lower "average rate of return" in exchange for downside protection and upside potential. The resulting portfolios are designed to meet individual investors' needs and preferences, as well as to protect individuals from Personal, Market and Aspirational risk factors. The Wealth Allocation Framework attempts to bring together MPT with aspects of Behavioral Finance through a single pragmatic Framework. A major conclusion of this work is that, for the individual investor, Risk Allocation should precede Asset Allocation.

The work of Ohlson (1995) and Feltham and Ohlson (1995) had a profound impact on accounting research in the 1990's. In this paper, we first discuss this valuation framework, identify its key features, and put it in the context of prior valuation models. We then review the numerous empirical studies that are based on these models. We find that most of these studies apply a residual income valuation model, without the information dynamics that are the key feature of the Feltham and Ohlson framework. We find that few studies have adequately evaluated the empirical validity of this framework. Moreover, the limited evidence on the validity of this valuation approach is mixed. We conclude that there are many opportunities to refine the theoretical framework and to test its empirical validity. Consequently, the praise many empiricists have given the models is premature.