We demonstrate that valuation uncertainty and information arrival are critical stock characteristics determining whether individual factors in leading factor models are influenced by sentiment or limited attention. Therefore, the ability of a factor model to explain cross-sectional stock returns depends on including two distinct types of factors: those that capture sentiment and those that tackle limited attention. Yet, many leading factor models include factors for sentiment but fall short in incorporating factors for limited attention. Our findings are important, guiding future research towards developing new factor models that more effectively capture both sentiment and limited attention compared to existing models. This includes uncovering powerful factors capable of simultaneously capturing sentiment and limited attention.

Stock market index enhancement remains a widely adopted strategy among hedge funds within China’s financial market. The underlying algorithm aims to fine-tune the weightings of individual stocks within a benchmark index, thereby enhancing the performance of the target portfolio relative to its original benchmark.Our innovative numerical framework stands out for its generality, rapidity, and theoretical convergence to the global optimum under reasonable assumptions. It also shines in tackling high-dimensional portfolio optimization problems. Empirical results demonstrate that the stock market index enhancement strategy, as computed by our algorithm, consistently delivers stable and significant excess returns, outperforming existing benchmarks.

Cryptocurrencies return cross-predictability and technological similarity yield information on risk propagation and market segmentation. To investigate these effects, we build a time-varying network for cryptocurrencies, based on the evolution of return cross-predictability and technological similarities. We develop a dynamic covariate-assisted spectral clustering method to consistently estimate the latent community structure of cryptocurrencies network that accounts for both sets of information. We demonstrate that investors can achieve better risk diversification by investing in cryptocurrencies from different communities. A cross-sectional portfolio that implements an inter-crypto momentum trading strategy earns a 1.08% daily return. By dissecting the portfolio returns on behavioral factors, we confirm that our results are not driven by behavioral mechanisms.

Using data from Binance, we find strong evidence of cross-cryptocurrency return predictability. The lagged returns of other cryptocurrencies serve as significant predictors of focal cryptocurrencies. The results are robust across various methods, including the adaptive LASSO and principal component analysis. Furthermore, a long-short portfolio formed on the past returns of cryptocurrencies can generate a sizable return out-of-sample after accounting for transaction costs. Overall, our findings corroborate cross-cryptocurrency return predictability and are consistent with the spillover effect mechanism, where common shocks among cryptocurrencies coupled with the limited attention of investors lead to slow information diffusion across coins.

Since the creation of Bitcoin in 2009, many alternative cryptocurrencies (altcoins) have been created that claim to be the currency of the future. There are now over 440 active altcoins in the market and new ones are being created each day. With every altcoin claiming to offer distinctive features, it is becoming increasingly difficult for investors to evaluate the potential of each altcoin. This chapter explores the various types of altcoins in the market and methods to evaluate their mid-term potential. The chapter will discuss a practical approach to rank the altcoins based on social network data and discuss the results from traditional econometric techniques to study the behavior of Bitcoin and altcoins.

加密货币是一类蓬勃发展的依托区块链架构设计的去中心化资产.同时,加密货币市场也伴随着较高的投机性和剧烈的价格波动.考虑到加密货币拥有的内在金融属性以及背后去中心化技术本身所具有的潜力,研究加密货币的风险与定价并进行加密货币市场的风险管理显得尤为重要.本文对加密货币市场的价格形成、加密货币的风险因子及收益率预测的相关研究进行梳理和分析.同时,本文对加密货币市场的交易行为及目前的监管政策和框架进行相应的分析与展望,说明了加密货币市场进行风险管理的必要性.中国政府需要在监管体系内最大程度发挥数字加密货币的价值,妥善处理好金融创新与监管之间的关系,进一步完善自身的加密货币监管框架.

This paper presents a novel exploration into anomaly factors in asset pric- ing by introducing the Empirical Mode Decomposition (EMD) method. Using a comprehensive dataset spanning 1974 to 2021 and focusing on 20 key charac- teristics, we apply EMD to uncover temporal dynamics within anomaly factors and to refine insights from high-dimensional factor data. The findings reveal the EMD method’s significant potential in enhancing out-of-sample predictions and optimizing portfolio outcomes, with a notable average return of 1.92% and a Sharpe ratio of 1.14, further indicating its superiority over Wavelet Decomposi- tion. We also uncover EMD’s proficiency in refining predictive information within characteristic sets and formulating impactful market fundamental and sentiment factors. Our paper provides a fresh viewpoint and methodology for analyzing multi-dimensional factor data and demonstrates the promising implications of EMD-processed factors for investment strategies.

The combination of artificial intelligence techniques and quantitative investment has given birth to various types of price prediction models based on machine learning algorithms. In this study, we verify the applicability of machine learning fused with statistical method models through the EMD-XGBoost model for stock price prediction. In the modeling process, specific solutions are proposed for overfitting problems that arise. The stock prediction model of machine learning fused with statistical learning was constructed from an empirical perspective, and an XGBoost algorithm model based on empirical modal decomposition was proposed. The data set selected for the experiment was the closing price of the CSI 300 index, and the model was judged by four indicators:mean absolute error, mean error, and root mean square error, etc. The method used for the experiment was the EMD-XGBoost network model, which had the following advantages: first, combining the empirical modal decomposition method with the XGBoost model is conducive to mining the time series data for Second, the decomposition of the CSI 300 index data by the empirical modal decomposition method is helpful to improve the accuracy of the XGBoost model for time series data prediction. The experiments show that the EMD-XGBoost model outperforms the single ARIMA or LSTM network model as well as the EMD-LSTM network model in terms of mean absolute error, mean error, and root mean square error.

We examine the relationship between two behavioral forces, sentiment and limited attention, and eight prominent factors that have recently been proposed to explain various anomalies. Investor sentiment explains many factors well, such as those related to equity issuance, because short legs are usually more speculative than long legs. Investor sentiment fails, however, to explain certain factors that are equally speculative with respect to the long legs and short legs, especially post-earnings-announcement drift (PEAD). Instead, market-wide attention affects these latter factors significantly but not those explained by investor sentiment. Our evidence illustrates the commonality and differences between recently proposed influential factors.

We find that a novel measure of aggregate joint news coverage of firms strongly and negatively predicts market returns with a monthly in-sample R-square of 3.93% and an out-of-sample R-square of 6.52%. The relation is causal, robust to existing market return predictors, and especially strong during periods of high market uncertainty or high market frictions. The Fama-Macbeth regression shows that a one-standard-deviation increase in joint news is associated with 20.3% more EDGAR downloads by new IPs from the connected firms via joint news. Our evidence is consistent with joint news generating a cross-firm investor attention spillover, resulting in high market valuations and subsequent low returns.

Media news may cover multiple firms in one article, which establishes a media connection across firms. We propose a media connection strength (MCS) measure between two given firms, which is defined as the number of news articles co-mentioning these two firms. We show that the MCS measure can significantly explain and forecast return comovement of media-connected firm-pairs. Further analyses show that our results are robust to various alternative explanations. We argue that the MCS measure can capture comprehensive and complex correlated fundamental information among media-connected firms and hence may provide a new mechanism for return comovement beyond the existing rational- and behavioral-based explanations.

We examine the value and efficiency of analyst recommendations through the lens of capital market anomalies. We find that analysts do not fully use the information in anomaly signals when making recommendations. Analysts tend to give more favorable consensus recommendations to stocks classified as overvalued and, more important, these stocks subsequently tend to have particularly negative abnormal returns. Analysts whose recommendations are better aligned with anomaly signals are more skilled and elicit stronger recommendation announcement returns. Our findings suggest that analysts’ biased recommendations could be a source of market friction that impedes the efficient correction of mispricing.

The 2017 bubble on the cryptocurrency market recalls our memory in the dot-com bubble, during which hard-to-measure fundamentals and investors’ illusion for brand new technologies led to overvalued prices. Benefiting from the massive increase in the volume of messages published on social media and message boards, we examine the impact of investor sentiment, conditional on bubble regimes, on cryptocurrencies aggregate return prediction. Constructing a crypto-specific lexicon and using a local-momentum autoregression model, we find that the sentiment effect is prolonged and sustained during the bubble while it turns out a reversal effect once the bubble collapsed. The out-of-sample analysis along with portfolio analysis is conducted in this study. When measuring investor sentiment for a new type of asset such as cryptocurrencies, we highlight that the impact of investor sentiment on cryptocurrency returns is conditional on bubble regimes.

In finance and accounting, relative to quantitative methods traditionally used, textual analysis becomes popular recently despite of its substantially less precise manner. In an overview of the literature, we describe various methods used in textual analysis, especially machine learning. By comparing their classification performance, we find that neural network outperforms many other machine learning techniques in classifying news category. Moreover, we highlight that there are many challenges left for future development of textual analysis, such as identifying multiple objects within one single document. to the Their trained Bayes algorithm categorize in analyst reports to categories: positive, and neutral. And they show that an additional positive is a significant impact on a firms earnings growth even five years after the publication of the

Practical Applications Summary In Cryptocurrency: A New Investment Opportunity? from the winter 2018 issue of The Journal of Alternative Investments, authors David LEE Kuo Chuen, Yu Wang (both of Singapore Management University) and Li Guo (of Singapore Management University) provide an in-depth introduction to Bitcoin and other cryptocurrencies, and explore their potential as an alternative investment class. Their results show that the return correlations between cryptocurrencies and traditional assets are low, and that adding the Cryptocurrency Index (CRIX) to a traditional asset portfolio helps diversify risk, and, under some circumstances, may improve overall performance. Given the difficulty of valuation in the cryptocurrency market, however, the authors explore the possibility of generating positive risk-adjusted profits with a “sentiment” strategy that evaluates each cryptocurrency individually. TOPICS:Currency, risk management, performance measurement, mutual funds/passive investing/indexing

We propose a crypto-specific lexicon to quantify the investor sentiment and use it to predict the cryptocurrency market returns. The new lexicon achieves better accuracy (32\% higher) than the traditional financial lexicon when applied to an out-of-sample classification setting. The empirical results reveal that investor sentiment positively predicts excess CRIX returns with a daily in(out)-of-sample $R^2$ of 2.74\% (3.15\%) without significant evidence on the return reversal. The results are robust to the inclusion of alternative sentiment indices, technical indicators, market microstructure noise, and across bubble and non-bubble periods. We further exclude the soft information interpretation of our sentiment index by showing that non-fundamental related sentiment shows stronger predictive power than that of the fundamentally related sentiment. Our findings suggest that in a market-driven by noise traders, and when there is only limited information about the fundamental value of the underlying asset, investor sentiment drives the price evolution and its impact may not reverse in a short horizon.