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Capital Markets: Market Efficiency eJournal最新文献

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Peer Momentum 同行的势头
Capital Markets: Market Efficiency eJournal Pub Date : 2021-06-23 DOI: 10.2139/ssrn.3747402
Efdal Misirli, Daniela Scida, Mihail Velikov
{"title":"Peer Momentum","authors":"Efdal Misirli, Daniela Scida, Mihail Velikov","doi":"10.2139/ssrn.3747402","DOIUrl":"https://doi.org/10.2139/ssrn.3747402","url":null,"abstract":"Using recent advances in network theory, we estimate the intra-industry connectedness for US publicly traded companies going back to the 1920s. We develop a stock-level composite centrality measure that captures multiple dimensions of a stock's interdependence with its industry peers. Using our network and composite centrality estimates, we develop \"peer momentum\" trading strategies, which sort stocks on their industry peers' past month average returns weighted by the peers' influence in the industry. A \"peripheral peer momentum\" strategy that uses only peripheral stocks' influence as weights for the signal construction achieves an annualized Sharpe ratio of 0.65, survives a battery of robustness tests, and helps explain industry momentum.","PeriodicalId":260048,"journal":{"name":"Capital Markets: Market Efficiency eJournal","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127085096","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}
引用次数: 0
Non-Financial Reporting And The Cost Of Capital In BRICS Countries 金砖国家的非财务报告和资本成本
Capital Markets: Market Efficiency eJournal Pub Date : 2021-06-23 DOI: 10.2139/ssrn.3872502
M. Evdokimova, S. Kuzubov
{"title":"Non-Financial Reporting And The Cost Of Capital In BRICS Countries","authors":"M. Evdokimova, S. Kuzubov","doi":"10.2139/ssrn.3872502","DOIUrl":"https://doi.org/10.2139/ssrn.3872502","url":null,"abstract":"This paper considers the impact of non-financial reporting (NFR) on the cost of capital (COC) in the forms of the cost of equity (COE), the cost of debt (COD), and the weighted average cost of equity (WACC). It was revealed that companies publishing non-financial reports have a lower COC. COD, COE, and WACC reduce after NFR. Six industries, where the cost of equity and debt capital is lower for companies publishing NFR, were determined: consumer discretionary, energy, industrials, information technology, healthcare, and materials. According to the analysis, companies that issued non-financial reports have a lower COE capital growth rate.","PeriodicalId":260048,"journal":{"name":"Capital Markets: Market Efficiency eJournal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129308830","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}
引用次数: 5
Does Mutual Fund Family Size Matter? International Evidence 共同基金家族规模重要吗?国际证据
Capital Markets: Market Efficiency eJournal Pub Date : 2021-06-19 DOI: 10.2139/ssrn.3470323
Yi-Hsuan Chen, A. Miguel, Xiayue Liu
{"title":"Does Mutual Fund Family Size Matter? International Evidence","authors":"Yi-Hsuan Chen, A. Miguel, Xiayue Liu","doi":"10.2139/ssrn.3470323","DOIUrl":"https://doi.org/10.2139/ssrn.3470323","url":null,"abstract":"Abstract We use data from 33 countries to study how a fund’s affiliation with large families shapes the flow–performance relationship internationally. Our results show that the effect of family size on the fund flows’ response to performance depends on the sophistication of investors in a country. While less sophisticated investors are persuaded by the great visibility and strategies of funds that are affiliated with large and established families, more sophisticated investors are not. Affiliation with a large family increases the convexity of the flow–performance relationship in countries where investors are less sophisticated, but decreases this convexity in countries with more sophisticated investors. These results are important for investors, mutual fund companies and regulators because the flow–performance sensitivity determines the assets under management, the level of fees, risk–taking, and the performance of the fund.","PeriodicalId":260048,"journal":{"name":"Capital Markets: Market Efficiency eJournal","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134539786","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}
引用次数: 7
What Can Capital Markets Teach Us About Learning? 关于学习，资本市场能教会我们什么?
Capital Markets: Market Efficiency eJournal Pub Date : 2021-06-17 DOI: 10.2139/ssrn.3864592
Kent Osband
{"title":"What Can Capital Markets Teach Us About Learning?","authors":"Kent Osband","doi":"10.2139/ssrn.3864592","DOIUrl":"https://doi.org/10.2139/ssrn.3864592","url":null,"abstract":"","PeriodicalId":260048,"journal":{"name":"Capital Markets: Market Efficiency eJournal","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121721922","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}
引用次数: 0
When Do Investors Know? Security Class Action Lawsuits, Short Selling, and Pre-filing News Releases 投资者什么时候知道?证券集体诉讼，卖空和预备案新闻发布
Capital Markets: Market Efficiency eJournal Pub Date : 2021-06-15 DOI: 10.2139/ssrn.3868949
Chris Stivers, Licheng Sun, Sounak Saha
{"title":"When Do Investors Know? Security Class Action Lawsuits, Short Selling, and Pre-filing News Releases","authors":"Chris Stivers, Licheng Sun, Sounak Saha","doi":"10.2139/ssrn.3868949","DOIUrl":"https://doi.org/10.2139/ssrn.3868949","url":null,"abstract":"Prior literature on security class-action lawsuits generally treats the lawsuit filing day as the day when the event become public, in terms of evaluating event-study returns and informed shorting activity. However, in the days prior to the lawsuit filing, our investigation reveals that there are public announcements of law-firm investigations into more than half of the sued firms. Over our 2009-2019 sample, accounting for these pre-filing investigation announcements weakens prior evidence that suggested short-selling investors tend to anticipate the lawsuit filing from private information or inference from related industry litigation. Strikingly, the average cumulative abnormal return over the 5-day pre-event filing window is about -6.5% for our full sample (similar to prior studies) but it is only about -2.2% when excluding the lawsuits with a recent pre-filing investigation announcement. Regarding responses to the public news releases, we find that: (1) the coincident abnormal short selling and negative abnormal returns are much larger for the higher quality lawsuits, and (2) the abnormal shorting coincident with the initial public news about the lawsuit is reliably negatively related to the post-news abnormal return. Thus, in addition to weakening an anticipatory shorting view, our evidence provides considerable support to conclusions in Engelberg, Reed, and Ringenberg (2012) - - that short sellers are skilled at analyzing publicly available information.","PeriodicalId":260048,"journal":{"name":"Capital Markets: Market Efficiency eJournal","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115622415","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}
引用次数: 0
The link between Bitcoin and Google Trends attention 比特币与谷歌趋势关注度之间的联系
Capital Markets: Market Efficiency eJournal Pub Date : 2021-06-13 DOI: 10.2139/ssrn.3866250
N. Aslanidis, A. F. Bariviera, Óscar G. López
{"title":"The link between Bitcoin and Google Trends attention","authors":"N. Aslanidis, A. F. Bariviera, Óscar G. López","doi":"10.2139/ssrn.3866250","DOIUrl":"https://doi.org/10.2139/ssrn.3866250","url":null,"abstract":"This paper shows that Bitcoin is not correlated to a general uncertainty index as measured by the Google Trends data of Castelnuovo and Tran (2017). Instead, Bitcoin is linked to a Google Trends attention measure specific for the cryptocurrency market. First, we find a bidirectional relationship between Google Trends attention and Bitcoin returns up to six days. Second, information flows from Bitcoin volatility to Google Trends attention seem to be larger than information flows in the other direction. These relations hold across different sub-periods and different compositions of the proposed Google Trends Cryptocurrency index.","PeriodicalId":260048,"journal":{"name":"Capital Markets: Market Efficiency eJournal","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115538636","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}
引用次数: 1
How do Investors React to Biased Information? Evidence from Chinese IPO Auctions 投资者对有偏见的信息有何反应?来自中国IPO拍卖的证据
Capital Markets: Market Efficiency eJournal Pub Date : 2021-06-11 DOI: 10.2139/ssrn.3864791
Jingbin He, Bo Liu, Yiyao Wang, Fei Wu
{"title":"How do Investors React to Biased Information? Evidence from Chinese IPO Auctions","authors":"Jingbin He, Bo Liu, Yiyao Wang, Fei Wu","doi":"10.2139/ssrn.3864791","DOIUrl":"https://doi.org/10.2139/ssrn.3864791","url":null,"abstract":"We study how institutional investors utilize potentially biased information by analyzing the effect of IPO underwriters' earnings forecasts on investors' bidding behaviors in Chinese IPO auctions. Despite the presence of upward biases in underwriters' earnings forecasts, we nd that investors' bid prices are higher in IPOs with higher earnings forecasts. The investors' positive reaction to biased information can be explained in a rational expectation model where the underwriter has valuable information about the IPO but has a biased incentive in presenting the information to investors. Consistent with the model's predictions, we find that an investor's bid price is more sensitive to the underwriter's earnings forecast when the forecast bias is expected to be smaller, when the relative precision of the underwriter's information over the investor's information is higher, and when the investor has a higher valuation of the IPO.","PeriodicalId":260048,"journal":{"name":"Capital Markets: Market Efficiency eJournal","volume":"2012 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121509742","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}
引用次数: 0
Deep Reinforcement Learning for Finance and the Efficient Market Hypothesis 金融的深度强化学习与有效市场假说
Capital Markets: Market Efficiency eJournal Pub Date : 2021-06-11 DOI: 10.2139/ssrn.3865019
L. Odermatt, Jetmir Beqiraj, Joerg Osterrieder
{"title":"Deep Reinforcement Learning for Finance and the Efficient Market Hypothesis","authors":"L. Odermatt, Jetmir Beqiraj, Joerg Osterrieder","doi":"10.2139/ssrn.3865019","DOIUrl":"https://doi.org/10.2139/ssrn.3865019","url":null,"abstract":"Is there an informational gain by training a Deep Reinforcement Learning agent for automated stock trading using other time series than the one to be traded? In this work, we implement a DRL algorithm in a solid framework within a model-free and actor-critic approach and learn it with 21 global Multi Assets to predict and trade on the S&amp;P 500. The Efficient Market Hypothesis sets out that it is impossible to gather more information from the broader input. We demand to learn a DRL agent on this index with and without the additional information of these several Multi Assets to determine if the agent could capture invisible dependencies to end up with an informational gain and a better performance.<br>The aim of this work is not to tune the hyperparameters of a DRL agent; several papers already exist on this subject. Nevertheless, we use a proven setup as model architecture. We take a Multi Layer Perceptron (short: MLP) as the neural network architecture with two hidden layers and 64 neurons each layer. The activation function used is the hyperbolic tangent. Further, Proximal Policy Optimization (short: PPO) is used as the policy for simple implementation and enabling a continuous state space. To deal with uncertainties of neural nets, we learn 100 agents for each scenario and compared both results. Neither the Sharpe ratios nor the cumulative returns are better in the more complex approach with the additional information of the Multi Assets, and even the single approach performed marginally better. However, we demonstrate that the complexly learned agent delivers less scattering over the 100 simulations in terms of the risk-adjusted returns, so there is an informational gain due to Multi Assets. A DRL agent learned with additional information delivers more robust results compared to the taken risk. We deliver valuable results for the further development of Deep Reinforcement Learning and provide a unique and resourceful approach.","PeriodicalId":260048,"journal":{"name":"Capital Markets: Market Efficiency eJournal","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131221011","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}
引用次数: 1
Enigmatic Forecasts of Enigmatic Risks 神秘风险的神秘预测
Capital Markets: Market Efficiency eJournal Pub Date : 2021-06-09 DOI: 10.2139/ssrn.3863662
Kent Osband
{"title":"Enigmatic Forecasts of Enigmatic Risks","authors":"Kent Osband","doi":"10.2139/ssrn.3863662","DOIUrl":"https://doi.org/10.2139/ssrn.3863662","url":null,"abstract":"Most risks in finance are chronically enigmatic. We cannot deduce them from symmetries,<br>gauge them precisely, or have confidence that they are stable. Yet capital markets force their<br>estimation and pricing. The excess volatility, propensity to turbulence, and high trading volumes<br>of capital markets have long been interpreted as evidence of arbitrary and irrational drivers. Yet<br>simulations and mathematical analysis show that rational learning about unstable risks induces<br>those features too. Indeed, a capital market can operate like a single rational mind even when<br>traders are fiercely competitive. This casts doubts on many presumptions of market irrationality.","PeriodicalId":260048,"journal":{"name":"Capital Markets: Market Efficiency eJournal","volume":"7 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134578778","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}
引用次数: 0
Planning in Financial Markets in Presence of Spikes: Using Machine Learning GBDT 金融市场中存在峰值的规划:使用机器学习GBDT
Capital Markets: Market Efficiency eJournal Pub Date : 2021-06-08 DOI: 10.2139/ssrn.3862428
E. Benhamou, J. Ohana, D. Saltiel, B. Guez
{"title":"Planning in Financial Markets in Presence of Spikes: Using Machine Learning GBDT","authors":"E. Benhamou, J. Ohana, D. Saltiel, B. Guez","doi":"10.2139/ssrn.3862428","DOIUrl":"https://doi.org/10.2139/ssrn.3862428","url":null,"abstract":"Planning in financial markets is a difficult task as the method needs to dramatically change its behavior when facing very rare black swan events like crises that shift market regime. In order to address this challenge, we present a gradient boosting decision trees (GBDT) approach to predict large price drops in equity indexes from a set of 150 technical, fundamental and macroeconomic features. We report an improved accu-racy of GBDT over other machine learning (ML) methods on the S&amp;P 500 futures prices. We show that retaining fewer and carefully selected features provides improvements across all ML approaches. We show that this model has a strong predic-tive power. We train the model from 2000 to 2014, a period where various crises have been observed and use a validation period of 3 years to find hyperparameters. The fitted model timely forecasts the Covid crisis giving us a planning method for early detection of potential future crises.","PeriodicalId":260048,"journal":{"name":"Capital Markets: Market Efficiency eJournal","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115520156","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}
引用次数: 1
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