Journal Publications

[18] Do ETFs affect ADRs and U.S. domestic stocks differently? (with Qiping Huang and Hongfei Tang). Journal of International Financial Markets, Institutions and Money, forthcoming.

[17] The Russia-Ukraine conflict and foreign stocks on the U.S. market (with Danjue Clancey-Shang). Journal of Risk Finance, forthcoming.

[16] The oil price plummeted in 2014-2015: Is there an effect on Chinese firms’ labor investment? (with Xinheng Liu, Shuxian Li, Xu Gong and Chen Fan). International Journal of Finance and Economics, forthcoming.

[15] The effect of COVID-19 on the relationship between idiosyncratic volatility and expected stock returns (with S. R. Tabatabaei Poudeh and Sungchul Choi). 2022. Risks, 10, 57. Download the Paper.

[14] The Cross-section of Expected Stock Returns and Components of Idiosyncratic Volatility (with S. R. Tabatabaei Poudeh). 2022. Journal of Risk Finance, 23, 403-417. Download the Paper.

[13] What drives oil prices? A Markov switching VAR approach (with Xu Gong, Keqin Guan, Liqing Chen and Tangyong Liu). 2021. Resources Policy, 74, 102316. Download the Paper.

[12] CSR disclosure of foreign versus U.S. firms: Evidence from ADRs (with Reza H. Chowdhury, Qiping Huang and Nanying Lin). 2021. Journal of International Financial Markets, Institutions and Money, 70, 101275. Download the Paper.

[11] Too High to Get it Right: The Effect of Cannabis Legalization on the Performance of Cannabis-Related Stocks (with Feilong Chen, Sungchul Choi and Joshua Nycholat). 2021. Economic Analysis and Policy, 72, 715-734. Download the Paper.

[10] Time-varying Risk and the Relation between Idiosyncratic Risk and Stock Return. 2021. Journal of Risk and Financial Management, 14, 9. Download the Paper.

[9] The Valuation of ADR IPOs (with Weidong Huo, Ying Huang and Steven Xiaofan Zheng). 2018. Journal of International Financial Markets, Institutions and Money, 53, 215-226. Download the Paper.

[8] Alpha Beta Risk and Stock Returns—A Decomposition Analysis of Idiosyncratic Volatility with Conditional Models. 2018. Risks, 6, 124. Download the Paper.

[7] Investor Sentiment and Portfolio Selection (with Gady Jacoby and Yan Wang). 2015. Finance Research Letters, 15, 266–273. Download the Paper.

Working Papers

[6] A Separation Analysis of the Idiosyncratic Volatility-Return Relation (with George Jiang, Gady Jacoby and Lei Lu).

We employ a two-step estimation method to separate the upside and downside idiosyncratic volatility and examine its relation with future stock returns. We find that idiosyncratic volatility is negatively related to stock returns when the market is up and when it is down. The upside idiosyncratic volatility is not related to stock returns. Our results also suggest that the relation between downside idiosyncratic volatility and future stock returns is negative and significant. It is the downside idiosyncratic volatility that drives the inverse relation between total idiosyncratic volatility and stock returns. The results are consistent with the literature that investor overreact to bad news and underreact to good news. Download the Paper.

[5] The Value of Investor Sophistication (with Gady Jacoby, Nanying Lin and Lei Lu).

We propose idiosyncratic volatility based return spread as a new measure of the stock-level value of investor sophistication. We find that stocks with a high value of investor sophistication tend to have low average returns, and this effect is pronounced for highly short-sale constrained stocks. The negative relation between expected stock returns and the value of investor sophistication is not explained by variables that affect the relation between idiosyncratic volatility and returns. Our results are robust with respect to whether idiosyncratic volatility is estimated using different factor models or methods. Such relation is more prominent in periods of high investor sentiment, high market uncertainty, and economic contraction. Download the Paper.

[4] International Political Uncertainty and Climate Risk Premium (with Xu Gong, Qiping Huang and Meimei Lin).

This paper investigates how political uncertainty affects firms’ climate risk premium from a global point of view. We use the presidential election events in the United States as well as that from all countries with a stock market as proxies for political uncertainty. We find that the global stock markets respond significantly to political uncertainty induced by the U.S. presidential elections, but not so for elections from their home countries. Although we do not observe a significant change in return premium for firms with different level of climate risk during the periods of political uncertainty, we find that firms with higher climate risk experience much higher return volatility and return correlation amid uncertainty associated with U.S. elections. The results are consistent with the literature that U.S. presidential election is a better indicator of international political uncertainty. At the same time, we uncover the new evidence on how political uncertainty affects the riskiness of firms with high exposure to climate risk. Download the Paper.

[3] CSR Disclosure, Political Risk and Market Quality: Evidence from the Russia-Ukraine Conflict (with Danjue Clancey-Shang)

In this study, we investigate how market quality diverges between high-ESG and low-ESG firms in the stock market in response to the Russia-Ukraine conflict. With an event-study approach, we find that better CSR performance alleviates the market quality deterioration associated with the outbreak of the war for US-listed foreign firms. Such an effect is insignificant for US domestic firms. We also find that foreign firms experience more severe market quality deterioration compared to their US counterparts. Our findings are consistent with the resiliency hypothesis concerning the CSR-financial performance link, as well as the remark that better CSR performance is associated with improved information transparency. Download the Paper.

[2] Stock Trend Prediction Framework based on Line Segment Algorithm and Deep Learning (with Zhaowei Liang, Fan Jiang and Liang Chen).

Stock price forecasting is a complicated task due to its volatile characteristics. How to effectively eliminate the fluctuation has attracted attention from both investors and researchers. This paper presents a novel technique named Line Segment Algorithm. Compared to those signal processing methods, it is based on the characteristic of financial time series. First, the algorithm identified the shape patterns of the historical stock price series and labeled them as turning points and false alarms. Then, a stock trend prediction framework was built and trained with the shape patterns extracted by the algorithm. Eventually, the model predicts whether a shape pattern is turning point or not. To evaluate its performance, experiments on the real stock data were carried out in LSTM and Random Forest, respectively. The results show that Line Segment Algorithm demonstrates its effectiveness by better accuracy on prediction. It provides a new perspective for stock trend analysis and can be applied in the actual stock investment trading as well.

[1] Sea Level Rise Risk and Mortgage Lending Standard (with Qiping Huang, Meimei Lin and Salman Tahsin).

We study the relationship between sea level rise (SLR) risk and access to residential mortgage credit at the census tract level from 2018 to 2020. Three different levels of SLR risk, ranging from imminent to long-term risks, are estimated using the elevations from sea level. We find significantly lower loan approval rates in census tracts that are exposed to all three different levels of SLR risks. Additionally, we find that the climate risk beliefs do not matter if the location is under imminent risk. However, in areas under medium or long-term risks, approval rates are affected by SLR only if climate risk beliefs are high. We also find that both local and diversified banks reduce loan acceptance rates in locations that are under imminent risk, but local banks approve significantly more loans if the risks are more medium to long term. The local bank effects are not driven by the size of banks. Overall, we uncover a significant impact of SLR risks on mortgage approval rates and we also find that the effects vary based on the level of SLR risks.

Grants and Awards

SSHRC GRF Grant, 2022-2023.

SSHRC Explore Grant, 2022-2023.

UNBC Research Strategic Initiatives Grant, 2022-2023.

SSHRC GRF Grant, 2021-2022.

Statistics Canada RDC Research Award, 2021-2022.

UNBC Pedagogical Development Fund, 2020.

UNBC Bridge Research Grant, 2019-2021.

UNBC Publication Grant, 2019.

UNBC Conference Travel Grant, 2018-2019.

UNBC Research Start-up Fund, 2018-2021.

Journal Reviewer

Accounting and Finance;

Applied Economics;

Applied Finance Letters;

Asia-Pacific Financial Markets;

China Finance Review International;

Cogent Economics and Finance;

Economics Letters;

European Journal of Finance;

Emerging Markets Review;

Emerging Markets Finance and Trade;

Finance Research Letters;

Financial Innovation;

International Review of Financial Analysis;

Journal of International Financial Markets, Institutions and Money;

Journal of Risk Finance;

Pacific-Basin Finance Journal;

Review of Financial Economics.