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on Financial Markets |
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Issue of 2024‒01‒22
seven papers chosen by |
| By: | Randall Morck; M. Deniz Yavuz |
| Abstract: | Savings increasingly flow to low-cost index funds, which simply buy and hold the stocks in a major index, such as the S&P 500. Increased indexing impedes incorporation of idiosyncratic information into stock prices. We limit endogeneity bias by showing that exogenous idiosyncratic currency shocks induce smaller idiosyncratic moves in the stock prices of currency-sensitive firms in proximate time windows when in the index than when not in it. Increased indexing thus appears to be undermining the efficient markets hypothesis that supports its viability. |
| JEL: | G11 G14 |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:31975&r=fmk |
| By: | Fu, Servanna Mianjun; Kellard, Neil; Verousis, Thanos; Kalaitzoglou, Iordanis |
| Abstract: | Using Trade and Quote (TAQ) data to infer variation in High frequency Trading (HFT) for the US equity markets and HFT start and colocation dates for a sample of 10 international exchanges, we find that increases in HFT activity lead to a significant increase in stock herding. The effect of HFT on herding is more pronounced for large-cap stocks, higher liquidity periods and during more volatile days. HFT activities are strongly associated with non-fundamental herding and encourage information cascades that induce price inefficiencies, suggesting changes to market design might be warranted. |
| Keywords: | High Frequency Trading; HFT; Herding; Colocation; Information cascades; Fundamental information |
| Date: | 2024–01–03 |
| URL: | http://d.repec.org/n?u=RePEc:esy:uefcwp:37485&r=fmk |
| By: | Ju-Hong Lee; Bayartsetseg Kalina; KwangTek Na |
| Abstract: | This paper explores the limitations of existing risk-adjusted returns in portfolio management and introduces a novel metric, the Market-adaptive ratio, to address these shortcomings. Existing risk-adjusted returns neglect the differences between bear and bull markets. Acknowledging that these market conditions demand distinct strategies, the Market-adaptive ratio incorporates the unique attributes of each, enhancing the portfolio performance. By emphasizing the significance of market type in impacting investment outcomes, this novel metric empowers investors to refine their strategies accordingly. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2312.13719&r=fmk |
| By: | Moro, Alessandro; Zaghini, Andrea |
| Abstract: | We propose a model with mean-variance foreign investors who exhibit a convex disutility associated to brown bond holdings. The model predicts that bond green premia should be smaller in economies with a closer financial account and highly volatile exchange rates. This happens because foreign intermediaries invest relatively less in such economies, and this lowers the marginal disutility of investing in polluting activities. We find strong empirical evidence in favor of this hypothesis using a global bond market dataset. Exchange rate volatility and financial account openness are thus able to explain the higher financing costs of green projects in emerging markets relative to advanced economies, especially when green bonds are denominated in local currency: a disadvantage that we can call the "green sin" of emerging economies. |
| Keywords: | Green bonds, Greenium, Exchange rate volatility, Financial openness, Original sin |
| JEL: | F21 F30 F31 G11 G12 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:cfswop:280929&r=fmk |
| By: | Hunold, Matthias; Werner, Tobias |
| Abstract: | This paper investigates the collusive and competitive effects of algorithmic price recommendations on market outcomes. These recommendations are often non-binding and common in many markets, especially on online platforms. We develop a theoretical framework and derive two algorithms that recommend collusive pricing strategies. Utilizing a laboratory experiment, we find that sellers condition their prices on the recommendation of the algorithms. The algorithm with a soft punishment strategy lowers market prices and has a pro-competitive effect. The algorithm that recommends a subgame perfect equilibrium strategy increases the range of market outcomes, including more collusive ones. Variations in economic preferences lead to heterogeneous treatment effects and explain the results. |
| Keywords: | Collusion, Experiment, Human-Machine Interaction, Bertrand Oligopoly |
| JEL: | C92 D43 L13 L41 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:dicedp:280937&r=fmk |
| By: | Martin Eling (University of St. Gallen); Anastasia V. Kartasheva (University of St. Gallen); Dingchen Ning (University of St. Gallen) |
| Abstract: | Cyber risk economic losses are large and growing, yet the insurance market for cyber risk is tiny, amounting to 0.4% ($2.8 billion) of premiums in the US property casualty insurance market in 2020. In this paper, we analyze the constraints that the insurance industry faces in providing larger capacity. We argue that cyber risk is special in that it combines (i) heavy-tailedness, (ii) uncertain loss distribution, and (iii) asymmetric information in underwriting. The combination of factors (i)-(iii) creates a tension between a need to raise substantial amounts of capital to finance heavy-tailed and uncertain risks and an expensive compensation demanded by investors due to information frictions. To circumvent asymmetric information costs, insurers can use internal capital. Hence, suppliers of cyber insurance are large insurance groups with a deep internal capital market. However, their capacity is constrained by the group’s size. We document stylized facts about the US cyber risk insurance market. We then establish the causal inference that insurers primarily rely on the internal capital market to supply cyber risk insurance using an exogenous shock of the non-US affiliated reinsurance tax treatment in 2017. Finally, we test which of the three features (i)–(iii) of cyber risk contribute to the cost of external capital and confirm that all of them play a significant role. |
| Keywords: | cyber risk insurance, large risks financing, internal capital market, reinsurance, information frictions |
| JEL: | G22 G32 L11 |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:chf:rpseri:rp23118&r=fmk |
| By: | Francesco Cesarone; Manuel Luis Martino; Federica Ricca; Andrea Scozzari |
| Abstract: | Sustainable Investing identifies the approach of investors whose aim is twofold: on the one hand, they want to achieve the best compromise between portfolio risk and return, but they also want to take into account the sustainability of their investment, assessed through some Environmental, Social, and Governance (ESG) criteria. The inclusion of sustainable goals in the portfolio selection process may have an actual impact on financial portfolio performance. ESG indices provided by the rating agencies are generally considered good proxies for the performance in sustainability of an investment, as well as, appropriate measures for Socially Responsible Investments (SRI) in the market. In this framework of analysis, the lack of alignment between ratings provided by different agencies is a crucial issue that inevitably undermines the robustness and reliability of these evaluation measures. In fact, the ESG rating disagreement may produce conflicting information, implying a difficulty for the investor in the portfolio ESG evaluation. This may cause underestimation or overestimation of the market opportunities for a sustainable investment. In this paper, we deal with a multi-criteria portfolio selection problem taking into account risk, return, and ESG criteria. For the ESG evaluation of the securities in the market, we consider more than one agency and propose a new approach to overcome the problem related to the disagreement between the ESG ratings by different agencies. We propose a nonlinear optimization model for our three-criteria portfolio selection problem. We show that it can be reformulated as an equivalent convex quadratic program by exploiting a technique known in the literature as the k-sum optimization strategy. An extensive empirical analysis of the performance of this model is provided on real-world financial data sets. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2312.10739&r=fmk |