{"title":"Arrow-Debreu Meets Kyle","authors":"C. Keller, M. Tseng","doi":"10.2139/ssrn.3862047","DOIUrl":"https://doi.org/10.2139/ssrn.3862047","url":null,"abstract":"We consider an extension of the Kyle (1985) model where Arrow-Debreu securities are traded and the informed trader has private information regarding arbitrary higher moments of the asset payoff distribution. In this setting, we analyze price discovery and informed demand of Arrow-Debreu securities---equivalently, options. The informed trader strategy in our model is consistent with options trading strategies used to trade on higher moments in practice. The probability law of market maker's posterior is independent of specification of asset payoffs. The information efficiency of Arrow-Debreu prices decreases with respect to the dispersion of the informed trader's private signal.","PeriodicalId":260048,"journal":{"name":"Capital Markets: Market Efficiency eJournal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129387882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Hedge Fund Alpha – Net Zero Using a Dynamic Factor Approach","authors":"Alex Lostado, L. Nilsson","doi":"10.2139/ssrn.3860453","DOIUrl":"https://doi.org/10.2139/ssrn.3860453","url":null,"abstract":"Using a novel database, the NilssonHedge hedge fund database covering more than 350,000 return observations, we perform a large-scale multiple regression. We evaluate alpha against the Fama French five-factor model including momentum. Our findings are compatible with a net-zero alpha from hedge funds after fees, assuming frictionless factor implementation. On the positive side, our analysis reveals a substantial divergence between funds, leaving room for timing and selection opportunities within most of the strategies.","PeriodicalId":260048,"journal":{"name":"Capital Markets: Market Efficiency eJournal","volume":"72 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114010443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Do Analysts Cater to Investor Beliefs? Evidence from Market Liberalization in China","authors":"Dawn Matsumoto, J. Zhang, Yuxiang Zheng","doi":"10.2139/ssrn.3864534","DOIUrl":"https://doi.org/10.2139/ssrn.3864534","url":null,"abstract":"We examine whether financial analysts cater to investors’ beliefs, using the market liberalization (Stock Connect) programs in China as a shock to investor beliefs. We find that analysts become less optimistic in their recommendations following the introduction of less optimistic investors through the Stock Connect programs. In addition, catering theory predicts that when investors hold heterogeneous beliefs, analysts tend to segment the market and slant toward extreme positions in order to attract target investors. Consistent with this prediction, we find that analyst dispersion increases in the post period. Moreover, analysts with buy or strong buy (sell or underperform) recommendations of a given firm become more optimistic (pessimistic) in their research report tone. Finally, we show that in updating their earnings forecasts, analysts are more (less) responsive to earnings surprises that are consistent (inconsistent) with their stock recommendations. Overall, this paper presents evidence supporting a catering theory for analyst bias.","PeriodicalId":260048,"journal":{"name":"Capital Markets: Market Efficiency eJournal","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125351501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The Invisible Portfolio","authors":"Sina Ehsani, Juhani T. Linnainmaa","doi":"10.2139/ssrn.3855066","DOIUrl":"https://doi.org/10.2139/ssrn.3855066","url":null,"abstract":"A portfolio sorted on the intercepts of a multi-factor model - the invisible portfolio - is the optimal portfolio for improving the model's mean-variance efficiency. This portfolio, similar to the betting-against-beta (BAB) factor, benefits from the distortions in the security market (or factor) lines. Whereas the BAB factor adjusts for the flatness in any one factor's security factor line, the invisible portfolio optimally adjusts for all such distortions. The invisible portfolio increases the five-factor model's out-of-sample maximum squared Sharpe ratio from 0.98 to 1.38. The invisible portfolio is an intuitive and theoretically founded method for improving all factor models.","PeriodicalId":260048,"journal":{"name":"Capital Markets: Market Efficiency eJournal","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126570535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Transparency and Liquidity in a Multi-Market Setting","authors":"P. Ghazizadeh, E. Peek, Dominik Rösch","doi":"10.2139/ssrn.3852831","DOIUrl":"https://doi.org/10.2139/ssrn.3852831","url":null,"abstract":"This study examines the effect of firm-level transparency on liquidity and trading in a multi-market setting, using the market for American Depository Receipts (ADR) as an example. Theory predicts competing effects of transparency on liquidity differences between stocks trading domestically and stocks trading in a foreign market, depending on why the stock trades in multiple markets. We show that an increase in transparency shifts part of foreign trading to the domestic market, yet improves foreign liquidity more than domestic liquidity and reduces foreign liquidity comovement. Collectively, our results show that transparency benefits foreign investors more than domestic investors and, consequently, may foster multi-market trading.","PeriodicalId":260048,"journal":{"name":"Capital Markets: Market Efficiency eJournal","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131979110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Terrorism and Patriotic Investors: Evidence from the 1920 Wall Street Bombing","authors":"D. Jansen","doi":"10.2139/ssrn.3852564","DOIUrl":"https://doi.org/10.2139/ssrn.3852564","url":null,"abstract":"This paper argues that patriotism does not necessarily account for market resilience in the face of terror. Studying the 1920 Wall Street bombing, I find that rallying-around-the-flag does not explain the upbeat nature of the first trading session after the attack. In fact, NYSE-traded firms with patriotic terms (such as \"American'' or \"U.S.'') in their name underperformed by 1 percentage point. The absence of a clear international actor behind the attack may explain the absence of patriotic trading. I also point out an alternative, economic reason for the upbeat market, which centers around Mexican Petroleum’s postponement of its ex-dividend date.","PeriodicalId":260048,"journal":{"name":"Capital Markets: Market Efficiency eJournal","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133210977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Technical Analysis in the Stock Market: A Review","authors":"Yufeng Han, Yang Liu, Guofu Zhou, Yingzi Zhu","doi":"10.2139/ssrn.3850494","DOIUrl":"https://doi.org/10.2139/ssrn.3850494","url":null,"abstract":"Technical analysis is the study for forecasting future asset prices with past data. In this survey, we review and extend studies on not only the time-series predictive power of technical indicators on the aggregated stock market and various portfolios, but also the cross-sectional predictability with various firm characteristics. While we focus on reviewing major academic research on using traditional technical indicators, but also discuss briefly recent studies that apply machine learning approaches, such as Lasso, neural network and genetic programming, to forecast returns both in the time-series and on the cross-section.","PeriodicalId":260048,"journal":{"name":"Capital Markets: Market Efficiency eJournal","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124488343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Undefine Chart Patters: An Image-Based Approach","authors":"Yannis Yuan","doi":"10.2139/ssrn.3935169","DOIUrl":"https://doi.org/10.2139/ssrn.3935169","url":null,"abstract":"Academic literature of asset technical analysis and price series pattern recognition typically involves characterising heuristic chart patterns with piecewise linear functions and filtering characteristic price sequences with local regression. We propose an image-based approach to encode price series as a two-dimensional density matrix and analyse price geometries with convolution filters. The method scales local prices based on volatility within a sliding window and creates a density array via one-hot encoding and aggregation. By comparing convolutional network classifiers and autoregressive networks, we show that the image-based price representation improves pattern-related feature interpretability. However, forecasting future price movements remains challenging due to global insignificance of consistent patterns. Period-adaptive pattern filters are necessary to denoise series and separate patterns prior to image-based encoding and forecasting.","PeriodicalId":260048,"journal":{"name":"Capital Markets: Market Efficiency eJournal","volume":"553 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116516339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"From Man vs. Machine to Man + Machine: The Art and AI of Stock Analyses","authors":"S. Cao, Wei Jiang, Junbo Wang, Baozhong Yang","doi":"10.2139/ssrn.3840538","DOIUrl":"https://doi.org/10.2139/ssrn.3840538","url":null,"abstract":"An AI analyst we build to digest corporate financial information, qualitative disclosure and macroeconomic indicators is able to beat the majority of human analysts in stock price forecasts and generate excess returns compared to following human analyst. In the contest of “man vs machine,” the relative advantage of the AI Analyst is stronger when the firm is complex, and when information is high-dimensional, transparent and voluminous. Human analysts remain competitive when critical information requires institutional knowledge (such as the nature of intangible assets). The edge of the AI over human analysts declines over time when analysts gain access to alternative data and to in-house AI resources. Combining AI’s computational power and the human art of understanding soft information produces the highest potential in generating accurate forecasts. Our paper portraits a future of “machine plus human” (instead of human displacement) in high-skill professions.","PeriodicalId":260048,"journal":{"name":"Capital Markets: Market Efficiency eJournal","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121076369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jeffrey A. Busse, Kiseon Chung, Badrinath Kottimukkalur
{"title":"Impediments to Active Stock Selection and the Growth in Passive Fund Management","authors":"Jeffrey A. Busse, Kiseon Chung, Badrinath Kottimukkalur","doi":"10.2139/ssrn.3833523","DOIUrl":"https://doi.org/10.2139/ssrn.3833523","url":null,"abstract":"During the last decade, assets of passive funds have swelled while active funds have lost market share. This growth in passive investments coincides with a substantially more challenging environment for active stock selection, as reflected in a lower fraction of stocks with positive alpha, lower idiosyncratic volatility, and higher aggregate liquidity. As the opportunities to discover alpha decrease, the relation between alpha and fund costs turns significantly negative. To compensate, active managers reduce expenses and fund turnover. Nonetheless, the inverse relation between fund costs and performance leads to greater predictability in fund performance, a stronger inverse relation between fund costs and investor flows, and greater sensitivity of investor flows to past performance.","PeriodicalId":260048,"journal":{"name":"Capital Markets: Market Efficiency eJournal","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134439494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}