Price Momentum Research Articles

The alpha momentum effect in commodity markets

We are the first to document an alpha momentum effect in commodity markets. We demonstrate a strong cross-sectional relationship between future commodity returns and past alphas derived from pricing models that control for major risk factors. A strategy of going long (short) in commodities with the highest (lowest) alphas delivers economically and statistically significant payoffs and produces higher risk-adjusted returns that are approximately double the standard price momentum. Alpha momentum is not subsumed by conventional risk factors, including price momentum although price momentum is subsumed by alpha momentum. Our results are robust to numerous considerations, including alternative evaluation models, different holding periods, trading costs, subperiod analysis and alternative implementation techniques.

Momentum, Echo and Predictability: Evidence from the London Stock Exchange (1820-1930)

We study momentum and its predictability within equities listed at the London Stock Exchange (1820-1930). At the time, this was the largest and most liquid stock market and it was thinly regulated, making for a good laboratory to perform out-of-sample tests. Cross-sectionally, we find that the size and market factors are highly profitable, while long-term reversals are not. Momentum is the most profitable and volatile factor. Its returns resemble an echo: they are high in long-term formation portfolios, and vanish in short-term ones. We uncover momentum in dividends as well. When controlling for dividend momentum, price momentum loses significance and profitability. In the time-series, despite the presence of a few momentum crashes, dynamically hedged portfolios do not improve the performance of static momentum. We conclude that momentum returns are not predictable in our sample, which casts some doubt on the success of dynamic hedging strategies.

Using Alternative Research Data in Real-World Portfolios

A number of market factors are generally acknowledged for having the potential to add alpha over the benchmark indexes over time. They include value, quality, price momentum, and low volatility. Exchange-traded funds have commoditized these factors, making them available at a very nominal fee. In contrast, nontraditional or alternative research data can be used to improve performance in both actively and passively managed portfolios, but obtaining and using these data can be challenging for many investment professionals. Challenges include knowing the sourcing methods for alternative data, understanding the mechanisms by which alternative data can and cannot generate alpha, identifying which types of investments benefit the most from nontraditional data, and learning how to implement alternative data strategies in real-world portfolios. This article is a primer for investment professionals seeking to learn more about how to best use alternative data sources, such as web scraping, as a new form of fundamental data for tactically or quantitatively managed active portfolios or as alternate selection and weighting strategies for tracking tolerant smart beta applications. TOPICS:Exchange-traded funds and applications, analysis of individual factors/risk premia, big data/machine learning, portfolio construction

Investor sentiment and its role in asset pricing: An empirical study for India

In this paper, we experiment with the construction of alternative investor sentiment indices. Further, we evaluate the role of the sentiment-based factor in asset pricing to explain prominent equity market anomalies such as size, value, and price momentum for India. Based on the findings, we confirm that our Composite Sentiment index leads other sentiment indices currently in vogue in investment literature. The asset pricing models, including the more recent Fama French 5 factor model, are not fully able to explain the small firm effect which is captured by our sentiment-based factor which seems to proxy for the price over-reactions.

Uncertainty, Momentum, and Profitability

In this article, the authors argue that momentum and profitability factors share a common source in uncertainty. Specifically, the authors find that uncertainty subsumes price momentum and operating profitability; it also accounts for the majority of the profits associated with earnings momentum and return on equity, especially in large firms. Furthermore, the profits of the four aforementioned momentum/profitability strategies concentrate in periods of negative market returns, consistent with high-uncertainty stocks’ greater vulnerability to bad market states documented in recent literature. The market-state dependence of momentum/profitability strategies has significant implications for portfolio managers who attempt to profit from these strategies. Understanding the sources of the profits also helps portfolio managers better employ these factors in constructing investment portfolios.

A comparison of investors’ and analysts’ efficiency in incorporating accounting information

ABSTRACT In this study, we compare the relative efficiency of investors and sell-side analysts in using various accounting information to predict future earnings. Our results indicate that the equity- value estimates inferred from the analysts’ earnings forecasts are more biased than the stock prices in interpreting stock price momentum, accruals, and the growth in long-term net operating assets. Therefore, we conclude that sell-side analysts are generally less efficient than investors in incorporating certain accounting information. Thus, investors may not be able to mitigate their mispricing of certain accounting information by fixating on analysts’ earnings forecasts as a benchmark for their earnings expectation.

The Value Uncertainty Premium

This paper investigates whether the time-series volatility of book-to-market (BM), called value uncertainty (UNC), is priced in the cross-section of equity returns. A size- adjusted value-weighted factor with a long (short) position in high-UNC (low-UNC) stocks generates an annualized alpha of 6-8%. This value uncertainty premium is driven by outperformance of high-UNC firms and is not explained by established risk factors or firm characteristics, such as price and earnings momentum, investment, profitability, or BM itself. At the aggregate level, UNC is correlated with macroeconomic fundamentals and predicts future market returns and market volatility. We provide a rational asset- pricing explanation of the uncovered UNC premium.

Investment-Momentum: A Two-Dimensional Behavioral Strategy

We propose an investment-momentum strategy of buying past winners with low investment and selling past losers with high investment, which exploits simultaneously two dimensions of market inefficiencies. The new strategy generates twice the monthly returns earned by either the price momentum or investment strategy (1.44% vs. 0.75% or 0.61%) for 1965-2015. Despite of the diminishing anomalies in recent decades, the investment-momentum stays persistent. The mispricing-based strategy performs better in periods of high investor sentiment or for stocks with high limits-to-arbitrage, which is consistent with our expectation. Overall, we show that, in addition to “fundamentals” enhanced momentum strategies, one can simultaneously condition on multi-dimension of inefficiencies to attain superior performance.

Decomposing the Earnings-to-Price Ratio and the Cross-section of International Equity-Index Returns

We examine whether components of the earnings-to-price (EP) ratio can be used to extract incremental information to better estimate future returns in the cross-section of country-industry indexes. We demonstrate that the EP components such as lagged EP, changes in earnings, short-term momentum, and long-term reversal in prices increase the accuracy of return forecasts. The EP decomposition matters in developed markets, however is pointless in emerging countries. The results are robust to modifications in the methodology, sub-period analyses, the use of an alternative sample and remain unchanged after controlling for net share issuance and size effects.

Stock Market Forecasting Using ant Colony Optimization Based Algorithm

Due to the importance of forecasting the capital market earnings in finance, recently the aspect of stock market prediction has been a major research area that has generated a lot of attention involving various machine learning algorithms. In the recent presentations, it has been indicated that neural networks have some drawbacks in learning the data patterns or that they may perform inconsistently and unpredictable because of the complexity of the stock market data. However, due to the distributive nature of the capital market, a computational intelligence technique called Ant Colony Optimization (ACO) which is suitable for solving distributed control problem was applied in this paper, to get the most optimal solution from three technical analysis strategies. The obtained optimal prediction of the next day closing stock price the ACO algorithm performs better than the other three approaches (Price Momentum Oscillator, Stochastic and Moving Average). Our algorithm (ACO based) was evaluated to have the accuracy of 0.812500, Sensitivity of 0.907407 and Specificity of 0.690476. The ACO based technique have the highest accuracy, Sensitivity and Specificity than the other three (3) technical indicators in predicting the next day closing stock price. Therefore, the optimal prediction of our ACO Agent provides a better forecast than the three initial strategies.

모멘텀 현상과 투자자 유형별 주식거래 행태의 관련성 분석

모멘텀 현상과 투자자 유형별 주식거래 행태의 관련성 분석

Down but not out: Plenty of returns available for shorted down stocks

Unlike in other developed equity markets, short sellers in Australia are required to report their covered short positions on a daily basis to the market regulator, who subsequently disseminates this information freely to the public in a very timely manner. If short selling contains negative information, a strategy that longs (shorts) stocks with low (high) short selling interest should result in positive returns. This study examines the profitability of such a strategy, named the short selling interest momentum trading strategy. The results indicate that significant returns can be made by following short sellers' actions ex post. Further, stronger returns are made from price momentum winner stocks and short sellers behave rationally towards short selling activities. The findings from this study are robust to various controls relating to size, industry, price momentum and options.

How Well the Implementation of Carhart Model in Market Overreaction Condition? Evidence in Indonesia Stock Exchange

This study aims to determine an ability of the four-factor model of Carhart in explaining the portfolio returns formed in condition of market overreaction. The four-factor model is basically a model proposed by Fama and French and then developed by Carhart which adds price momentum factor into the model. While market overreaction is a market condition caused by excessive reactions from investors when receiving information. The portfolios used are the winner and loser formed based on the returns of each portfolio to the average of the returns. Both portfolio consist are the stocks of non-financial sector in Indonesia Stock Exchange during the period July 2005 - December 2015. The data used are the Composite Stock Price Index (CSPI), stock market capitalization, book to market ratio of each shares and the difference of returns of the loser over of the winner, as an indicator of price momentum factor that formed in market overreaction condition characterized by occurance the reversal of returns.The results show that the four-factor model can explain the portfolio return well. Implementation of the GARCH (1,1) model to improve the accuracy of the estimation results also shows similar findings.

Asset growth, style investing, and momentum

Abstract We establish a significant and robust connection between asset growth (AG) and style investing by showing that past style returns constructed based on AG and size jointly predict future stock returns significantly. Motivated by this notion, we propose a style momentum strategy based on AG and size and find that it dominates price momentum and size-BM style momentum in generating momentum profits. We examine two explanations for this predictability, including risk exposure to common risk factors and the limited-attention theory. Empirical evidence shows that the AG-size style momentum profit is induced because investors neglect the AG-size style performance, consistent with the limited-attention explanation, but not risk exposure to the investment factor. Further, we show that the profit of the AG-size style momentum is robust to different time periods partitioned by several time-series predictors.

Underreaction to Political Information and Price Momentum

AbstractIn this study, we examine whether momentum in stock prices is induced by changes in the political environment. We find that momentum profits are concentrated among politically sensitive firms and industries. From 1939 to 2016, a trading strategy with a long position in winner portfolios (industries or firms) that are politically unfavored and a short position in losers that are politically favored does not generate significant momentum profits. Furthermore, our political‐sensitivity‐based long‐short portfolio explains 23% to 27% (42% to 43%) of monthly stock (industry) momentum alphas. This explanatory power is concentrated around presidential elections, when the level of political activity is high. Collectively, our results suggest that investor underreaction to political information generates momentum in stock and industry returns.

The Impact of Volatility on Returns Models: The Case of Crude Oil

This article demonstrates that a static model of crude oil price movements, in which the various predictive variables always have the same weights, is not the ideal approach to model crude oil prices. Rather, investors looking to invest in crude oil need to understand how volatility affects the extent to which the various predictive variables matter for crude oil prices during their forecast horizon. Specifically, the relationships between crude oil returns and crude oil production and between crude oil returns and crude oil price momentum are a function of oil price volatility; the higher crude oil price volatility, the more negative the relationships between crude oil returns and production and between crude oil returns and price momentum. In addition, the higher crude oil prices, the more negatively crude oil returns are related to changes in crude oil volatility. One conclusion is that, although production, price momentum, and volatility all matter for crude oil prices, they matter differently at different times. A second conclusion is that, because there is a greater ability to forecast large moves in crude oil prices when volatility is higher, it is sensible for investors to be more active in crude oil markets when volatility is higher. Extrapolating from the results reported for crude oil, one would expect fundamental and technical data to have greater absolute effects on returns for industrial metals, agricultural products, and other commodity markets when volatility is high. It follows that traders should be more active in these markets when volatility is higher.

The Conservative Formula: Quantitative Investing Made Easy

The authors propose a conservative investment formula that selects 100 stocks based on three criteria: low return volatility, high net payout yield, and strong price momentum. They show that this simple formula gives investors full and efficient exposure to the most important factor premiums and thus effectively summarizes half a century of empirical asset pricing research in one easy to implement investment strategy. With a compounded annual return of 15.1% since 1929, the conservative formula outperforms the market by a wide margin. It reduces downside risk and shows a positive return over every decade. The formula is also strong in European, Japanese, and emerging stock markets, and it beats a wide range of other strategies based on combinations of size, value, quality, and momentum. The formula is designed to be a practically useful tool for a broad range of investors and addresses academic concerns about p-hacking by using three simple criteria that do not even require accounting data.

Price momentum and the premium for meeting or beating analysts' forecasts of earnings

This study provides a theoretical rationale and empirical support that relates the existence and magnitude of the premium for meeting/beating analysts' EPS forecasts to the existence of preannouncement price momentum. The study is based on the theoretical work that suggests that extreme levels of price momentum can cause security prices to deviate from fundamental values even in the presence of well-informed and well-financed rational arbitrageurs. Differences of opinion regarding the extent of mispricing and/or optimal exit time to exit the position allow this mispricing to persist (Abreu and Brunnermeir 2002, 2003). To correct mispricing, a news event, like an earnings announcement, is necessary to synchronize investors' exit strategy beliefs (Abreu and Brunnermeir 2002, 2003). In the case of an earnings announcement, this synchronization of beliefs triggers a price reaction of such magnitude that it cannot be explained by unexpected earnings. Instead, we hypothesize and show that the abnormal price reaction is largely captured in what empirical researchers have identified as the meet/beat market premium. Our findings provide a cohesive argument for the temporal variation in meet/beat premiums documented by Koh, Matsumoto and Rajgopal (2008).

Momentum and Reversal in Cryptocurrency Markets

If behavioral biases explain asset pricing anomalies, they should also materialize in cryptocurrency markets. I test more than 20 stock return anomalies based on daily cryptocurrency data, and document strong evidence of price momentum. Controlling for market and size, price momentum remains statistically significant, whereas price reversal and risk-based anomalies are weak. Cryptocurrency anomalies can be explained by behavioral theories that emphasize noise trader risks than fundamental risks.

Residual momentum versus price momentum: evidence from four Asian markets‡

This paper investigates the profitability of momentum investment strategies – residual momentum versus price momentum – for common stocks listed in Hong Kong, Singapore, Taiwan, and Thailand. This study shows that the residual-momentum winner and loser on the previous residual returns derived under the CAPM have lower time-varying exposures to the market risk. The residual momentum strategy generates not only higher but also more consistent returns over time than the price momentum strategy. Moreover, the residual momentum effect exists in the four aforementioned stock markets, whereas there is no evidence for the price momentum.