YANDI ZHU | 朱彦頔

Short-term Basis Reversal

with Alberto Rossi and Yingguang Zhang

Abstract:We identify a previously undocumented form of return predictability in commodity futures markets, which we refer to as short-term basis reversal: The return spread between adjacent maturity contracts exhibits systematic negative autocorrelation. Basis reversal is independent of the short-term reversal of the individual contracts. Instead, it stems from the differential price sensitivity to news across the futures curve and is stronger among futures contracts with higher return volatility, more autocorrelated returns, and less correlated returns across maturities. Consistent with a preferred-habitat and limits to arbitrage interpretation, we show that basis reversal is also present in other assets characterized by a term structure, such as stock index futures, corporate bonds, and treasury bonds.

Does Average Skewness Really Matter?

with Yunting Liu (draft available soon)

5. Man versus Machine Learning Revisited

Review of Financial Studies, Forthcoming (with Yingguang Zhang and Juhani Linnainmaa)

[code] [data]

Abstract: Binsbergen et al. (2023) predict analysts' forecast errors using a random forest model. A strategy that trades against this model's predictions earns a monthly alpha of 1.54% (t-value = 5.84). This estimate represents a large improvement over studies using classical statistical methods. We attribute the difference to a look-ahead bias. Removing the bias erases the alpha. Linear models yield as accurate forecasts and superior trading profits. Neither alternative machine learning models nor combinations thereof resurrect the predictability. We discuss the state of research into the term structure of analysts' forecasts and its causal relationship with returns.

4. Good Idiosyncratic Volatility, Bad Idiosyncratic Volatility, and the Cross-Section of Stock Returns

Journal of Banking and Finance, 2025 (with Yunting Liu)

Abstract: We decompose the idiosyncratic volatility of stock returns into “good” and “bad” volatility components, which are associated with positive and negative returns, respectively. Using firm characteristics, we estimate a cross-sectional model for the expected idiosyncratic good minus bad volatility (EIGMB). The EIGMB outperforms expected idiosyncratic skewness (EISKEW) and standard time-series models in capturing conditional idiosyncratic return asymmetry. EIGMB is negatively and significantly associated with future stock returns, even after controlling for EIKSEW and exposure to systematic-skewness-related factors. Separating the role each specific characteristic plays in driving the predictive power of EIGMB for returns, we find that return on equity and momentum are two important elements of variation in EIGMB.

3. Expectation Disarray: Analysts' Growth Forecast Anomaly in China

Pacific-Basin Finance Journal, 2023 (with Laura Xiaolei Liu, Xinyu Zhang, and Yingguang Zhang)

Abstract: Analysts' growth forecasts positively predict stock returns in China, opposite to the results found in the US. Strategies that buy stocks with high growth forecasts and sell those with low growth forecasts earn annual abnormal returns of up to 20% (t-values exceeding three). These results are stronger for longer-horizon forecasts and ex-ante more informative forecasts. In addition, the deviation of analysts' forecasts from unbiased forecasts based on statistical models positively predicts abnormal returns. Although the relationship between analysts' forecasts and returns in China is opposite to that in the US, these forecasts positively predict actual growth and are often too extreme, as in the US. Our results suggest that investors in China overlook valuable information contained in analysts' forecasts.

2. 中国经济微观不确定性的测度及效应研究 China's Micro Uncertainty and its Effect (In Chinese)

《经济学动态》 Economic Perspectives, 2023 (with Yunting Liu, featured cover article)

中国人民大学复印报刊资料全文转载, 《国民经济管理》2023年第7期

摘要： 本文以2003-2021年我国A股上市企业的财务数据为样本，对中国微观经济不确定性进行测度并研究其对企业层面投资及宏观经济的效应。研究发现，微观不确定性提高会抑制企业投资水平，但这种抑制作用随着时间延长削弱，并在中长期反转。微观不确定性在短期主要通过实物期权与金融摩擦两种渠道影响企业投资水平。本文提出并验证了微观不确定性的资源分配渠道，此渠道在中长期会促进企业投资。微观不确定性在宏观层面效应与在微观方面一致，且表现为总需求冲击。本文深化了经济不确定性的度量及传导机制研究。在政策启示上，政策需要区分不确定性来源，关注企业层面不确定性，并且适度包容和引导市场中资源再分配，以为市场经济保驾护航。

1. Joint dynamic topic model for recognition of lead-lag relationship in two text corpora

Data Mining and Knowledge Discovery, 2022 (with Xiaoling Lu, Jingya Hong, and Feifei Wang)

Abstract: In this work, we focus on a special type of relationship between two text corpora, which we define as the "lead-lag relationship." This relationship characterizes the phenomenon that one text corpus would influence the topics to be discussed in the other text corpus in the future. To discover the lead-lag relationship, we propose a joint dynamic topic model and also develop an embedding extension to address the modeling problem of large-scale text corpus. With the recognized lead-lag relationship, the similarities of the two text corpora can be figured out and the quality of topic learning in both corpora can be improved. We numerically investigate the performance of the joint dynamic topic modeling approach using synthetic data. Finally, we apply the proposed model on two text corpora consisting of statistical papers and the graduation theses. Results show the proposed model can well recognize the lead-lag relationship between the two corpora, and the specific and shared topic patterns in the two corpora are also discovered.