{"id":57,"date":"2014-09-16T12:18:13","date_gmt":"2014-09-16T16:18:13","guid":{"rendered":"http:\/\/belkcollegeofbusiness.charlotte.edu\/ichiang1\/?page_id=57"},"modified":"2025-06-09T10:00:46","modified_gmt":"2025-06-09T14:00:46","slug":"research","status":"publish","type":"page","link":"https:\/\/belkcollegeofbusiness.charlotte.edu\/ichiang1\/research\/","title":{"rendered":"Research"},"content":{"rendered":"<p><strong><span style=\"font-family: Verdana\">PUBLICATIONS<\/span><\/strong><\/p>\n<hr \/>\n<p><span style=\"font-size: small\"><b><span style=\"color: #0000ff\"><a style=\"color: #0000ff\" href=\"https:\/\/www.ssrn.com\/abstract=3393265\"><span style=\"font-family: Verdana\">A &#8220;bad beta, good beta&#8221; anatomy of currency risk premiums and trading strategies<\/span><\/a><\/span><span style=\"font-family: Verdana\">, <\/span><\/b><span style=\"font-family: Verdana\">by I-Hsuan Ethan Chiang and Xi Nancy Mo, <em>Review of Finance<\/em>, forthcoming.<\/span><\/span><\/p>\n<ul>\n<li><strong><span style=\"font-size: small\"><span style=\"font-family: Verdana\">2019 China Finance Review International Conference Best Paper Award<\/span><\/span><\/strong><\/li>\n<li><strong><span style=\"font-size: small\"><span style=\"font-family: Verdana\">2019 <\/span><\/span><\/strong><strong><span style=\"font-size: small\"><span style=\"font-family: Verdana\">FMA Best Paper in Financial Institutions &amp; Markets, Finalist<\/span><\/span><\/strong><strong><span style=\"font-size: small\"><span style=\"font-family: Verdana\">\u00a0<\/span><\/span><\/strong><\/li>\n<\/ul>\n<p><span style=\"font-family: Verdana;font-size: small\">We test a two-beta currency pricing model that features betas with risk-premium news and real-rate news of the currency market. Unconditionally, beta with currency market risk-premium news is &#8220;bad&#8221; because of significantly positive price of risk (2.52% per year); beta with global real-rate news is &#8220;good&#8221; due to nearly zero or negative price of risk. The price of risk-premium beta risk is counter-cyclical, while the price of the real-rate beta risk is pro-cyclical. Most prevailing currency trading strategies either have excessive &#8220;bad beta&#8221; or too little &#8220;good beta,&#8221; failing to deliver abnormal performance. Our empirical results can be delivered by a no-arbitrage model with precautionary savings and a pricing kernel characterized by two separate global shocks.<\/span><\/p>\n<hr \/>\n<p><span style=\"font-size: small\"><b><span style=\"color: #0000ff\"><a style=\"color: #0000ff\" href=\"https:\/\/doi.org\/10.1016\/j.pacfin.2025.102784\"><span style=\"font-family: Verdana\">The impact of investor attention on mispricing of dual-listed shares: Evidence from Chinese A-share and H-share markets<\/span><\/a><\/span><span style=\"font-family: Verdana\">, <\/span><\/b><span style=\"font-family: Verdana\">by Wei-Ling Huang, I-Hsuan Ethan Chiang, and Ming-Hung Wu, 2025, <em>Pacific-Basin Finance Journal<\/em>\u00a092, 102784.\u00a0\u00a0<\/span><\/span><\/p>\n<p><span style=\"font-family: Verdana;font-size: small\">This study examines the relation between investor attention and the A-H share price premium in Mainland China stock markets (which list A shares) and Hong Kong stock markets (which list H shares of the same company) using the Baidu search index (BSI) as a proxy for retail investor attention. Our findings indicate that a one-standard-deviation increase in the abnormal BSI (ABSI) corresponds to a significant increase of 1.90% in the next-day premium between A-H share prices. This finding suggests that increased attention from retail investors causes overpricing, resulting in higher A-H share price premiums. Notably, we observe that mobile searches have a stronger effect on price premiums than do personal computer (PC) searches. A one-standard-deviation increase in the PC-based ABSI leads to a 1.30% increase, whereas the mobile-based ABSI results in a 1.86% increase in the A-H share price premium. Additionally, ABSI-driven mispricing lasts 60 trading days until prices converge. Moreover, we find that greater institutional investor ownership reduces ABSI-driven mispricing. Finally, the influence of investor attention on the A-H share price premium diminishes following the implementation of the 2014 Shanghai\/Hong Kong Stock Connect Policy, which allows global investors to invest in both the Shanghai and Hong Kong markets. We contribute to the literature by highlighting novel factors affecting the A-H share price premium, including the influence of investor attention, device use, and institutional investor activity.<\/span><\/p>\n<hr \/>\n<p><span style=\"font-size: small\"><b><span style=\"color: #0000ff\"><a style=\"color: #0000ff\" href=\"https:\/\/doi.org\/10.1016\/j.jbankfin.2021.106068\"><span style=\"font-family: Verdana\">Short-term reversals, short-term momentum, and news-driven trading activity<\/span><\/a><\/span><span style=\"font-family: Verdana\">, <\/span><\/b><span style=\"font-family: Verdana\">by I-Hsuan Ethan Chiang, Chris Kirby, and Ziye Zoe Nie, 2021, <em>Journal of Banking &amp; Finance <\/em>125,  106068.\u00a0\u00a0<\/span><\/span><\/p>\n<p><span style=\"font-family: Verdana;font-size: small\">We find no evidence of monthly return reversals for the top quintile of small- and large-cap stocks ranked by turnover. Indeed, stocks in the top decile of turnover display short-term momentum. We argue these findings arise from a combination of effects. First, short-term reversals stem from short-term liquidity demands. Second, news-driven returns tend to continue rather than reverse. Third, turnover acts as a proxy for both liquidity and news-driven trading activity. The evidence suggests that reversals give way to momentum as turnover increases because high-turnover stocks are more liquid than low-turnover stocks and their returns are more reflective of news-driven trading activity. For example, the correlation between the monthly returns of stocks that announce earnings during the month and their announcement-window returns increases with monthly turnover. Furthermore, sorting stocks into turnover-based portfolios that are rebalanced monthly leads to a disproportionate number of stocks with earnings announcements in the high-turnover portfolios.<\/span><\/p>\n<hr \/>\n<p><span style=\"font-size: small\"><b><a href=\"https:\/\/doi.org\/10.1016\/j.jempfin.2020.11.003\"><span style=\"color: #0000ff\"><span style=\"font-family: Verdana\">Modeling the cross section of stock returns using sensible models in a model pool<\/span><\/span><\/a><span style=\"font-family: Verdana\">, <\/span><\/b><span style=\"font-family: Verdana\">by I-Hsuan Ethan Chiang, Yin Liao, and Qing Zhou, 2021, <em>Journal of Empirical Finance <\/em>60, 50-73.<\/span><\/span><\/p>\n<p><span style=\"font-family: Verdana;font-size: small\">An increase in the number of asset pricing models intensifies model uncertainties in asset pricing.\u00a0 While a pure &#8220;model selection&#8221; (singling out a best model) can result in a loss of useful information, a full &#8220;model pooling&#8221; may increase the risk of including noisy information.\u00a0 We make a trade-off between the two methods and develop a new two-step trimming-then-pooling method to forecast the joint distributions of asset returns using a large pool of asset pricing models.\u00a0 Our method allows investors to focus on certain regions of the distributions.\u00a0 In the first step, we trim the uninformative models from a pool of candidates, and in the second step, we pool the forecasts of the surviving models.\u00a0 We find that our method significantly enhances portfolio performance and predicts downside risk precisely, and the improvements are mainly due to trimming.\u00a0 The pool of sensible models becomes larger when focusing on extreme events, responds rapidly to rising uncertainty, and reflects the magnitude of factor premiums.\u00a0 These findings provide new insights into asset pricing model evaluation.<\/span><\/p>\n<hr \/>\n<p><span style=\"font-size: small\"><b><span style=\"color: #0000ff\"><a style=\"color: #0000ff\" href=\"https:\/\/doi.org\/10.1093\/rapstu\/raz002\"><span style=\"font-family: Verdana\">Real exchange rates and currency risk premiums<\/span><\/a><\/span><span style=\"font-family: Verdana\">, <\/span><\/b><span style=\"font-family: Verdana\">by Pierluigi Balduzzi and I-Hsuan Ethan Chiang, 2020, <em>Review of Asset Pricing Studies <\/em>10: 94-121.<\/span><\/span><\/p>\n<ul>\n<li><strong><span style=\"font-size: small\"><span style=\"font-family: Verdana\">EFA Outstanding Paper in International Finance\u00a0<\/span><\/span><\/strong><\/li>\n<li><strong><span style=\"font-size: small\"><span style=\"font-family: Verdana\">FMA Best\u00a0Paper in Investments, Finalist<\/span><\/span><\/strong><\/li>\n<li><strong><span style=\"font-size: small\"><span style=\"font-family: Verdana\">FMA Top 10\u00a0 Session<\/span><\/span><\/strong><\/li>\n<\/ul>\n<p><span style=\"font-family: Verdana;font-size: small\">Standard finite-horizon tests uncover only weak evidence of predictive power of the real exchange rate for excess currency returns.\u00a0 On the other hand, in long-horizon tests, the real exchange rate predicts strongly and negatively future excess currency returns.\u00a0 Conversely, we can attribute most of the variability of real exchange rates to changes in currency risk premiums. The &#8220;habit&#8221; and &#8220;long-run risks&#8221; models replicate the predictive power of the real exchange rate for excess currency returns, but substantially overstate the fraction of the volatility of the real exchange rate due to risk premiums.<\/span><\/p>\n<hr \/>\n<p><span style=\"font-size: small\"><b><span style=\"color: #0000ff\"><a style=\"color: #0000ff\" href=\"http:\/\/www.sciencedirect.com\/science\/article\/pii\/S0378426617300468\"><span style=\"font-family: Verdana\">Do oil futures prices predict stock returns?<\/span><\/a><\/span><span style=\"font-family: Verdana\">, <\/span><\/b><span style=\"font-family: Verdana\">by I-Hsuan Ethan Chiang and Keener Hughen, 2017, <em>Journal of Banking &amp; Finance<\/em>\u00a079<em>,<\/em>\u00a0129-141.<\/span><\/span><\/p>\n<p><span style=\"font-family: Verdana;font-size: small\">This paper explores stock return predictability by exploiting the cross-section of oil futures prices. Motivated by the principal component analysis, we find the curvature factor of the oil futures curve predicts monthly stock returns: a 1% per month increase in the curvature factor predicts 0:4% per month decrease in stock market index return. This predictive pattern is prevailing in non-oil industry portfolios, but is absent for oil-related portfolios. The in- and out-of-sample predictive power of the curvature factor for non-oil stocks is robust and outperforms many other predictors, including oil spot prices. The predictive power of the curvature factor comes from its ability to forecast supply-side oil shocks, which only affect non-oil stocks and are hedged by oil-related stocks.<\/span><\/p>\n<hr align=\"left\" \/>\n<p><span style=\"font-size: small\"><b><span style=\"color: #0000ff\"><a style=\"color: #0000ff\" href=\"http:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/jfir.12093\/abstract\"><span style=\"font-family: Verdana\">Skewness and coskewness in bond returns<\/span><\/a><\/span><span style=\"font-family: Verdana\">, <\/span><\/b><span style=\"font-family: Verdana\">by I-Hsuan Ethan Chiang, 2016,<em>\u00a0Journal of Financial Research\u00a0<\/em>39, 145-178.<\/span><\/span><\/p>\n<ul>\n<li><strong><span style=\"font-size: small\"><span style=\"font-family: Verdana\">FMA Best Dissertation Proposal in Fixed Income Research Award<\/span><\/span><\/strong><\/li>\n<\/ul>\n<p><span style=\"font-family: Verdana;font-size: small\">Bond skewness and coskewness (i.e., bond return co-movement with market volatility)\u00a0are both time-varying, with cross-sectional variation driven by maturity and credit rating. \u00a0Other things being equal, longer maturity bonds have lower skewness, and lower coskewness\u00a0with respect to the bond market index; lower quality bonds have lower skewness, and higher\u00a0coskewness with respect to the bond market index. Three-moment bond alphas (that\u00a0account for coskewness effects) are time-varying and predictable by market default spread. \u00a0They are significantly different from, and often are closer to zero than, two-moment alphas\u00a0(that ignore coskewness effects).<\/span><\/p>\n<hr \/>\n<p><span style=\"font-size: small\"><b><span style=\"color: #0000ff\"><a style=\"color: #0000ff\" href=\"http:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/jofi.12222\/full\"><span style=\"font-family: Verdana\">Estimating oil risk factors using information from equity and derivatives markets<\/span><\/a><\/span><span style=\"font-family: Verdana\">, <\/span><\/b><span style=\"font-family: Verdana\">by I-Hsuan Ethan Chiang, W. Keener Hughen, and Jacob S. Sagi,\u00a02015, <em>Journal of Finance<\/em> 70, 769-804.<\/span><\/span><\/p>\n<ul>\n<li><strong><span style=\"font-size: small\"><span style=\"font-family: Verdana\">Belk College of Business Faculty Best Paper Award<\/span><\/span><\/strong><\/li>\n<\/ul>\n<p><span style=\"font-family: Verdana;font-size: small\">We introduce a novel approach to estimating latent oil risk factors and establish their significance in pricing non-oil securities. Each of the four factors in our new model for commodity prices has a simple economic interpretation. The model is fitted to oil futures prices, option-implied variance and oil-related equity returns, and performs very well both in and out of sample. Using equity data in the estimation increases the fitted R-squared of oil-related equity returns by more than 20 percentage points. The oil factors are associated with a significant risk premium and are shown to be systematic. Specifically, the factors are significantly related to important macroeconomic variables and the average non-oil industry and characteristic portfolio exhibits a sensitivity to the oil factors amounting to a sixth (in magnitude) of that of the oil industry itself.<\/span><\/p>\n<hr align=\"left\" \/>\n<p><span style=\"font-size: small\"><b><span style=\"color: #0000ff\"><a style=\"color: #0000ff\" href=\"http:\/\/dx.doi.org\/10.1016\/j.jempfin.2015.05.003\"><span style=\"font-family: Verdana\">Modern portfolio management with conditioning information<\/span><\/a><\/span><span style=\"font-family: Verdana\">, <\/span><\/b><span style=\"font-family: Verdana\">by I-Hsuan Ethan Chiang, 2015, <em>Journal of Empirical Finance<\/em> 33, 114-134.<\/span><\/span><\/p>\n<ul>\n<li><strong><span style=\"font-size: small\"><span style=\"font-family: Verdana\">INFORMS Financial Services Section Best Student Research Paper Award, Runner-up<\/span><\/span><\/strong><\/li>\n<\/ul>\n<p><span style=\"font-family: Verdana;font-size: small\">This paper studies models in which active portfolio managers utilize conditioning information unavailable to their clients to optimize performance relative to a benchmark. We derive explicit solutions for the optimal strategies with multiple risky assets, with or without a risk-free asset, and consider various constraints on portfolio risks or weights. The optimal strategies feature a mean-variance efficient component (to minimize portfolio variance), and a hedging demand for the benchmark portfolio (to maximize correlation with the benchmark). A currency portfolio example shows that the optimal strategies improve the measured performance by 53% out of sample, compared with portfolios ignoring conditioning information.<\/span><\/p>\n<hr \/>\n<p><span style=\"font-size: small\"><b><span style=\"color: #0000ff\"><a style=\"color: #0000ff\" href=\"http:\/\/raps.oxfordjournals.org\/content\/2\/2\/203.abstract\"><span style=\"font-family: Verdana\">A simple test of the affine class of term structure models<\/span><\/a><\/span><span style=\"font-family: Verdana\">, <\/span><\/b><span style=\"font-family: Verdana\">by Pierluigi Balduzzi and I-Hsuan Ethan Chiang, 2012, <em>Review of Asset Pricing Studies<\/em> 2: 203-244 .<\/span><\/span><\/p>\n<p><span style=\"font-family: Verdana;font-size: small\">Affine term structure models imply an affine relation between yields and factors, and between yields and yields. Hence, a necessary condition for the affine class to hold is that yield changes are linearly related to changes in as many other yields as the number of underlying risk factors. At the same time, yield changes should be unrelated to changes in nonlinear transformations of other yields. We test this hypothesis using weekly data on U.S. Treasury yields for the June 1961&#8211;December 2002 sample period. Bootstrap-adjusted tests lead to only weak rejections of the affine class, and a simulation shows that these tests have correct size and high power. Imposing the cross-equation restrictions deriving from a no-arbitrage affine term structure model leads to stronger rejections, but these stronger rejections have more to do with the no-arbitrage restrictions than with the implication of linearity. In an out-of-sample hedging exercise, the constant hedge ratios implied by the affine class generally outperform time-varying hedge ratios implied by nonlinear models.<\/span><\/p>\n<hr \/>\n<p><span style=\"font-size: small\"><b><span style=\"color: #0000ff\"><a style=\"color: #0000ff\" href=\"http:\/\/www.emeraldinsight.com\/journals.htm?articleid=17014839\"><span style=\"font-family: Verdana\">REIT performance and market timing ability<\/span><\/a><\/span><span style=\"font-family: Verdana\">, <\/span><\/b><span style=\"font-family: Verdana\">by Richard Buttimer, Jun Chen, and I-Hsuan Ethan Chiang, 2012, <em>Managerial Finance<\/em> 38, 249-279.<\/span><\/span><\/p>\n<ul>\n<li><strong><span style=\"font-size: small\"><span style=\"font-family: Verdana\">EFA Outstanding Paper in\u00a0Real Estate<\/span><\/span><\/strong><\/li>\n<\/ul>\n<p><span style=\"font-family: Verdana;font-size: small\">This paper studies performance and market timing ability of equity real estate investment trusts (REITs). Using Treynor and Mazuy (1966) and Henriksson and Merton (1981) market timing models and their multi-index, multifactor, and conditional extensions, we find that equity REITs in aggregate have some housing market timing ability. Various equity REIT subcategories perform differently: office REITs can discover underpriced properties, while retail, industrial, and office REITs have poor timing ability. Nonparametric tests confirm that equity REITs do not have ability to predict real estate market movements.<\/span><\/p>\n<hr \/>\n<p><strong><span style=\"font-family: Verdana\">Select Working Papers<\/span><\/strong><\/p>\n<hr \/>\n<p><span style=\"font-size: small\"><b><span style=\"font-family: Verdana\">\u201cThe Carbon Policy Paradox: Divergent Impacts of Short-term vs. Long-term Policies\u201d<\/span><span style=\"font-family: Verdana\">, <\/span><\/b><span style=\"font-family: Verdana\">by I-Hsuan Ethan Chiang, Shih-Kuei Lin, and Zong-Wei Yeh.<\/span><\/span><\/p>\n\n<p><span style=\"font-size: small\"><b><span style=\"font-family: Verdana\">\u201cAnother Dog That Could Have Barked: A Critique of the Log-linear Present-value Models\u201d<\/span><span style=\"font-family: Verdana\">, <\/span><\/b><span style=\"font-family: Verdana\">by I-Hsuan Ethan Chiang and Xi Nancy Mo.<\/span><\/span><\/p>\n\n<p><span style=\"font-size: small\"><b><span style=\"font-family: Verdana\">\u201cWhat Drives Cryptocurrency Prices?\u201d<\/span><span style=\"font-family: Verdana\">, <\/span><\/b><span style=\"font-family: Verdana\">by I-Hsuan Ethan Chiang, Mao-Wei Hung, Xi Nancy Mo, and Ken Pang-Yu Wang.<\/span><\/span><\/p>\n\n<hr \/>\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>PUBLICATIONS A &#8220;bad beta, good beta&#8221; anatomy of currency risk premiums and trading strategies, by I-Hsuan Ethan Chiang and Xi Nancy Mo, Review of Finance, forthcoming. 2019 China Finance Review International Conference Best Paper Award 2019 FMA Best Paper in Financial Institutions &amp; Markets, Finalist\u00a0 We test a two-beta currency pricing model that features betas [&hellip;]<\/p>\n","protected":false},"author":1473,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"jetpack_post_was_ever_published":false,"footnotes":""},"class_list":["post-57","page","type-page","status-publish","hentry"],"jetpack_shortlink":"https:\/\/wp.me\/P54aTB-V","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/belkcollegeofbusiness.charlotte.edu\/ichiang1\/wp-json\/wp\/v2\/pages\/57","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/belkcollegeofbusiness.charlotte.edu\/ichiang1\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/belkcollegeofbusiness.charlotte.edu\/ichiang1\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/belkcollegeofbusiness.charlotte.edu\/ichiang1\/wp-json\/wp\/v2\/users\/1473"}],"replies":[{"embeddable":true,"href":"https:\/\/belkcollegeofbusiness.charlotte.edu\/ichiang1\/wp-json\/wp\/v2\/comments?post=57"}],"version-history":[{"count":100,"href":"https:\/\/belkcollegeofbusiness.charlotte.edu\/ichiang1\/wp-json\/wp\/v2\/pages\/57\/revisions"}],"predecessor-version":[{"id":820,"href":"https:\/\/belkcollegeofbusiness.charlotte.edu\/ichiang1\/wp-json\/wp\/v2\/pages\/57\/revisions\/820"}],"wp:attachment":[{"href":"https:\/\/belkcollegeofbusiness.charlotte.edu\/ichiang1\/wp-json\/wp\/v2\/media?parent=57"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}