管理层盈余预告消息性质与预告方式操控性选择

文章利用我国A股上市公司2004年1季度－2007年4季度管理层盈余预告样本数据,检验了盈余预告消息性质对管理层盈余预告披露精确性、及时性及态度倾向选择的影响。检验的结果表明：管理层对消息性质为好消息的盈余预告比坏消息的选择了更为精确的预告方式;而对坏消息的盈余预告态度倾向于欠悲观或相对乐观。实证结果说明,管理层在对不同性质消息的盈余预告过程中存在一定程度的操控性披露选择。     

MEAN‐VARIANCE POLICY FOR DISCRETE‐TIME CONE‐CONSTRAINED MARKETS: TIME CONSISTENCY IN EFFICIENCY AND THE MINIMUM‐VARIANCE SIGNED SUPERMARTINGALE MEASURE

The discrete‐time mean‐variance portfolio selection formulation, which is a representative of general dynamic mean‐risk portfolio selection problems, typically does not satisfy time consistency in efficiency (TCIE), i.e., a truncated precommitted efficient policy may become inefficient for the corresponding truncated problem. In this paper, we analytically investigate the effect of portfolio constraints on the TCIE of convex cone‐constrained markets. More specifically, we derive semi‐analytical expressions for the precommitted efficient mean‐variance policy and the minimum‐variance signed supermartingale measure (VSSM) and examine their relationship. Our analysis shows that the precommitted discrete‐time efficient mean‐variance policy satisfies TCIE if and only if the conditional expectation of the density of the VSSM (with respect to the original probability measure) is nonnegative, or once the conditional expectation becomes negative, it remains at the same negative value until the terminal time. Our finding indicates that the TCIE property depends only on the basic market setting, including portfolio constraints. This motivates us to establish a general procedure for constructing TCIE dynamic portfolio selection problems by introducing suitable portfolio constraints.     

The determinants of price discovery on bitcoin markets

This paper investigates whether market quality, uncertainty, investor sentiment and attention, and macroeconomic news affect bitcoin price discovery in spot and futures markets. Over the period December 2017–March 2019, we find significant time variation in the contribution to price discovery of the two markets. Increases in price discovery are mainly driven by relative trading costs and volume, and uncertainty to a lesser extent. Additionally, medium-sized trades contain most information in terms of price discovery. Finally, higher news-based bitcoin sentiment increases the informational role of the futures market, while attention and macroeconomic news have no impact on price discovery.     

CVaR HEDGING USING QUANTIZATION‐BASED STOCHASTIC APPROXIMATION ALGORITHM

In this paper, we investigate a method based on risk minimization to hedge observable but nontradable source of risk on financial or energy markets. The optimal portfolio strategy is obtained by minimizing dynamically the conditional value‐at‐risk (CVaR) using three main tools: a stochastic approximation algorithm, optimal quantization, and variance reduction techniques (importance sampling and linear control variable), as the quantities of interest are naturally related to rare events. As a first step, we investigate the problem of CVaR regression, which corresponds to a static portfolio strategy where the number of units of each tradable assets is fixed at time 0 and remains unchanged till maturity. We devise a stochastic approximation algorithm and study its a.s. convergence and weak convergence rate. Then, we extend our approach to the dynamic case under the assumption that the process modeling the nontradable source of risk and financial assets prices is Markovian. Finally, we illustrate our approach by considering several portfolios in connection with energy markets.     

MULTIVARIATE RISK MEASURES: A CONSTRUCTIVE APPROACH BASED ON SELECTIONS

Since risky positions in multivariate portfolios can be offset by various choices of capital requirements that depend on the exchange rules and related transaction costs, it is natural to assume that the risk measures of random vectors are set‐valued. Furthermore, it is reasonable to include the exchange rules in the argument of the risk measure and so consider risk measures of set‐valued portfolios. This situation includes the classical Kabanov's transaction costs model, where the set‐valued portfolio is given by the sum of a random vector and an exchange cone, but also a number of further cases of additional liquidity constraints. We suggest a definition of the risk measure based on calling a set‐valued portfolio acceptable if it possesses a selection with all individually acceptable marginals. The obtained selection risk measure is coherent (or convex), law invariant, and has values being upper convex closed sets. We describe the dual representation of the selection risk measure and suggest efficient ways of approximating it from below and from above. In the case of Kabanov's exchange cone model, it is shown how the selection risk measure relates to the set‐valued risk measures considered by Kulikov (2008, Theory Probab. Appl. 52, 614–635), Hamel and Heyde (2010, SIAM J. Financ. Math. 1, 66–95), and Hamel, Heyde, and Rudloff (2013, Math. Financ. Econ. 5, 1–28).     

Clustering in emerging equity markets

We consider pairwise tail behavior of return series for identifying the most important emerging markets clusters. Pairs of markets belonging to the same group present similar type and strength of interdependence during stressful times, represented by a common copula and a statistically equivalent measure of tail dependence. By collapsing data from d markets in to a group we overcome the difficult problem of finding their (higher dimensional) d-variate distribution. Results may help portfolio managers to deal with risk due to co-movements within clusters. We provide examples on how this can be done. Our study contributes to the discussion about the international association among stock markets during turbulent periods, and does not confirm the intuition that the observed association between extremes should be credited to linkages to leading markets. The study also confirms the importance of stock selection, particularly among the non-dominant stocks, instead of holding market-value weighed portfolios of stocks from countries within the same region.     

A symmetric LPM model for heuristic mean-semivariance analysis

While the semivariance (lower partial moment degree 2) has been variously described as being more in line with investors’ attitude towards risk, implementation in a forecasting portfolio management role has been hampered by computational problems. The original formulation by Markowitz (1959) requires a laborious iterative process because the cosemivariance matrix is endogenous and a closed form solution does not exist. There have been attempts at optimizing an exogenous asymmetric cosemivariance matrix. However, this approach does not always provide a positive semi-definite matrix for which a closed form solution exists. We provide a proof that converts the exogenous asymmetric matrix to a symmetric matrix for which a closed form solution does exist. This approach allows the mean-semivariance formulation to be solved using Markowitz's critical line algorithm. Empirical results compare the cosemivariance algorithm to the covariance algorithm which is currently the best optimization proxy for the cosemivariance. We also compare our formulation to Estrada's (2008) cosemivariance formulation. The results demonstrate that the cosemivariance algorithm is robust to a 45 security universe and is still effective at increasing portfolio skewness at a 150 security universe. There are four major benefits to a usable mean-semivariance formulation: (1) managers may engineer skewness into the portfolio without resorting to option strategies, (2) managers will be able to evaluate the skewness effect of option strategies within their portfolio, (3) a workable mean-semivariance algorithm leads to a workable n-degree lower partial moment (LPM) algorithms which provides managers access to a wider variety of investor utility functions including risk averse, risk neutral, and risk seeking utility functions, and (4) a workable LPM algorithm leads to a workable UPM/LPM (upper partial moment/lower partial moment) algorithm.     

Continuous‐time mean–variance portfolio selection: A reinforcement learning framework

We approach the continuous‐time mean–variance portfolio selection with reinforcement learning (RL). The problem is to achieve the best trade‐off between exploration and exploitation, and is formulated as an entropy‐regularized, relaxed stochastic control problem. We prove that the optimal feedback policy for this problem must be Gaussian, with time‐decaying variance. We then prove a policy improvement theorem, based on which we devise an implementable RL algorithm. We find that our algorithm and its variant outperform both traditional and deep neural network based algorithms in our simulation and empirical studies.     

AN ONLINE PORTFOLIO SELECTION ALGORITHM WITH REGRET LOGARITHMIC IN PRICE VARIATION

We present a novel efficient algorithm for portfolio selection which theoretically attains two desirable properties:

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Bond Rate,Loan Rate and Tobin's q in a Temporary Equilibrium Model of the Financial Sector

The paper sets up a portfolio model of the financial sector with markets for equity, government bonds, money and debt. The comparative statics of the temporary equilibrium are studied analytically and numerically. Subsequent simulations explore the reactions of financial markets in response to stylized oscillations of some of the exogenous variables. These include economic activity, income distribution, inflation, investors' sentiment, and banks' perceived bankruptcy risk of firms. Special emphasis is put on the resulting cyclical pattern of Tobin's q and the interest spread between loan rate and bond rate.     

Semistatic hedging and pricing American floating strike lookback options

We price an American floating strike lookback option under the Black–Scholes model with a hypothetic static hedging portfolio (HSHP) composed of nontradable European options. Our approach is more efficient than the tree methods because recalculating the option prices is much quicker. Applying put–call duality to an HSHP yields a tradable semistatic hedging portfolio (SSHP). Numerical results indicate that an SSHP has better hedging performance than a delta-hedged portfolio. Finally, we investigate the model risk for SSHP under a stochastic volatility assumption and find that the model risk is related to the correlation between asset price and volatility.     

Retweet if you please! Do news factors explain engagement?

Stakeholders’ engagement with corporate messages is crucial for companies that want to increase their social media coverage. However, our understanding of the content-related factors that drive stakeholders’ engagement is still poor. Drawing from the literature on news selection and distribution, this paper explores whether the relevant news values that are already found in corporate messages could also explain greater engagement in social media. In particular, six news factors were examined in a sample of 2331 tweets that were posted by the Euro Stoxx 50 companies. The results show that four news factors (in particular, Prominence, Entertainment, Immediacy, and to a lesser extent Human interest) play a role in the drive of content sharing through retweets and favorites. Additionally, the inclusion of hashtags and media content helps increase retweets and favorites. Finally, we observe differences among industries. In particular, firms operating in business-to-business industries obtain less reach through sharing. The implications of these results are relevant for social media strategists as they suggest avenues to increase followers’ engagement with company-produced content.     

On-Line Portfolio Selection Using Multiplicative Updates

We present an on-line investment algorithm that achieves almost the same wealth as the best constant-rebalanced portfolio determined in hindsight from the actual market outcomes. The algorithm employs a multiplicative update rule derived using a framework introduced by Kivinen and Warmuth. Our algorithm is very simple to implement and requires only constant storage and computing time per stock in each trading period. We tested the performance of our algorithm on real stock data from the New York Stock Exchange accumulated during a 22-year period. On these data, our algorithm clearly outperforms the best single stock as well as Cover's universal portfolio selection algorithm. We also present results for the situation in which the investor has access to additional "side information."     

Model-free portfolio theory: A rough path approach

Based on a rough path foundation, we develop a model-free approach to stochastic portfolio theory (SPT). Our approach allows to handle significantly more general portfolios compared to previous model-free approaches based on Föllmer integration. Without the assumption of any underlying probabilistic model, we prove a pathwise formula for the relative wealth process, which reduces in the special case of functionally generated portfolios to a pathwise version of the so-called master formula of classical SPT. We show that the appropriately scaled asymptotic growth rate of a far reaching generalization of Cover's universal portfolio based on controlled paths coincides with that of the best retrospectively chosen portfolio within this class. We provide several novel results concerning rough integration, and highlight the advantages of the rough path approach by showing that (nonfunctionally generated) log-optimal portfolios in an ergodic Itô diffusion setting have the same asymptotic growth rate as Cover's universal portfolio and the best retrospectively chosen one.     

The role of tail network topological characteristic in portfolio selection: A TNA-PMC model

To improve the performance of a large portfolio selection, we consider the effect of tail network and propose a novel tail network-augmented parametric mean-conditional value-at-risk (CVaR) portfolio selection model labeled as TNA-PMC. First, we adopt the least absolute shrinkage and selection operator-quantile vector autoregression (LASSO-QVAR) approach to construct a tail network. Second, we parameterize the weights of the mean-CVaR model as a function of asset characteristics. Third, we incorporate the effect of the tail network topological characteristic, namely eigenvector centrality (EC), on the weights to construct the TNA-PMC model. After that, we apply the model to the empirical analysis on the Shanghai Stock Exchange 50 (SSE50) Index of China from January 2010 to September 2020. Our empirical results illustrate the effectiveness of the TNA-PMC model in two aspects. First, the TNA-PMC model clarifies the economic interpretation of the characteristics, such as the negative effective of EC on the portfolio weights. Second, the TNA-PMC model performs well in terms of achieving diversification and attractive risk-adjusted return.     

The accelerating effect of institutional environment unfamiliarity on subsidiary portfolio expansion in a new host country

In forming external networks in a host country, a foreign subsidiary acquires knowledge which contributes to the parent’s stock of country-specific knowledge and capabilities. Building on this notion, we argue that an MNE unfamiliar with the institutional environment of a newly entered foreign country has an incentive to expand its subsidiary portfolio in that country at a higher rate. We test this using a longitudinal dataset of German public firms which made investments into 2220 new host countries between 1985 and 2015. We find that unfamiliarity accelerates subsidiary portfolio expansion most when the initial investment is a wholly-owned subsidiary, and that the effect becomes weaker over time. Our study contributes to the internationalization process literature by showing that being unfamiliar with a host country is not necessarily an obstacle to subsidiary portfolio expansion – indeed it can accelerate it.     

OPTIMAL LOT SOLUTION TO CARDINALITY CONSTRAINED MEAN–VARIANCE FORMULATION FOR PORTFOLIO SELECTION

The pioneering work of the mean–variance formulation proposed by Markowitz in the 1950s has provided a scientific foundation for modern portfolio selection. Although the trade practice often confines portfolio selection with certain discrete features, the existing theory and solution methodologies of portfolio selection have been primarily developed for the continuous solution of the portfolio policy that could be far away from the real integer optimum. We consider in this paper an exact solution algorithm in obtaining an optimal lot solution to cardinality constrained mean–variance formulation for portfolio selection under concave transaction costs. Specifically, a convergent Lagrangian and contour-domain cut method is proposed for solving this class of discrete-feature constrained portfolio selection problems by exploiting some prominent features of the mean–variance formulation and the portfolio model under consideration. Computational results are reported using data from the Hong Kong stock market.     

The quantile dependence of commodity futures markets on news sentiment

Focusing on energy commodities, industrial metals, and gold, this paper examines the degree to which commodity futures returns depend on news sentiment under various market conditions, and the structure of that dependence. We observe an asymmetric market reaction to positive and negative news sentiment, which changes in periods of financial turmoil. The quantile regression analysis shows that news sentiment's influence on the futures returns follows an upward trend at higher percentiles. This structure flattens for positive news during the global financial crisis, while the slope for the negative component steepens in backwardation periods.     

‘We (don’t) know how you feel’ – a comparative study of automated vs. manual analysis of social media conversations

Abstract

The ever-growing volume of brand-related conversations on social media platforms has captivated the attention of academics and practitioners, as the analysis of those conversations promises to offer unparalleled insight into consumers’ emotions. This article takes a step back from the hype, and investigates the vulnerabilities related to the analysis of social media data concerning consumers’ sentiment. A review of the literature indicates that the form, focus, source and context of the communication may negatively impact on the analyst’s ability to identify sentiment polarity and emotional state. Likewise, the selection of analytical tool, the creation of codes, and the classification of the data, adversely affect the researcher’s ability to accurately assess the sentiment expressed in a social media conversation. Our study of Twitter conversations about coffee shows low levels of agreement between manual and automated analysis, which is of grave concern given the popularity of the latter in consumer research.     

A Dynamic Volume–Return Relation and Investors' Positive Feedback Trading

With individual stocks, a larger increase in trading volume indicates a stronger short‐term return persistence. A reason for this short‐horizon ‘volume–return relation’ is that it can signal the existence of fundamental news, which can be gradually incorporated into stock price. In this study, we present another plausible explanation by considering investors' short‐term positive feedback trading. First, through empirical analysis, we show that the volume–return relation remains strong among stocks for which there is little fundamental news. Through a model‐based analysis, we demonstrate that positive feedback trading can cause this relation even when there is no news. Our findings raise the possibility that the short‐horizon volume–return relation is also caused by short‐term positive feedback trading.