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on Financial Markets |
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Issue of 2023‒02‒27
eleven papers chosen by |
| By: | Giuliano Graziani; Barbara Rindi |
| Abstract: | We consider a model of a limit order book and determine the optimal tick size set by a social planner who maximizes the welfare of market participants. In a 2-period model where only two agents arrive sequentially, the tick size is a friction that constrains investors to use discrete price grids, and as a consequence the optimal tick size is equal to zero. However, in a model with sequential arrival of more than two investors who can endogenously either take liquidity or supply liquidity by undercutting or queuing behind existing orders, the tick size is positive: it is a strategic tool a social planner uses to optimally affect the choice made by investors between liquidity demand and supply. In addition, the optimal tick size is a function both of the value of the asset and of trading volume. The policy implication of such findings is that the European tick size regime and the “Intelligent Ticks” Nasdaq proposal dominate Reg. NMS Rule 612 that formalizes the tick size regime for the U.S. markets. Using data from the U.S. and the European markets we test our model’s empirical predictions. Keywords: Limit Order Book, Tick Size, Social Planner, Undercutting, Queuing. |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:igi:igierp:688&r=fmk |
| By: | Zarifhonarvar, Ali |
| Abstract: | In financial economics, numerous theoretical models explain the relationship between investment risk and return in the capital market, one of the most common being the Capital Asset Pricing Model (CAPM). After reviewing the literature in this area, this study discusses the theoretical background of the CAPM model. After explaining the relationship between systematic corporate risk in different industries, the hypotheses for a positive linear correlation between stock returns and systematic risk and the relation of these coefficients to the CAPM model predictions are tested. Thus, after data sampling to obtain the monthly rate of return of stocks in the Tehran Stock Exchange, the monthly rate of return of the market portfolio and the return on risk-free investment are obtained from April 2008 to March 2013. Finally, it will be shown that the systematic risk variable and its square are also crucial to explaining stock return fluctuations. A nonlinear quadratic correlation is confirmed between the rate of return and systematic risk in the stock data of companies sampled from the obtained sample of the Tehran Stock Exchange. |
| Keywords: | CAPM, Beta, Stock Market, Premium, Risk |
| JEL: | G10 G11 G12 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:esprep:268396&r=fmk |
| By: | Dyck, Alexander; Morse, Adair; Zingales, Luigi |
| Abstract: | We provide a lower-bound estimate of the undetected share of corporate fraud. To identify the hidden part of the "iceberg, " we exploit Arthur Andersen's demise, which triggered added scrutiny on Arthur Andersen's former clients and thereby increased the detection likelihood of preexisting frauds. Our evidence suggests that in normal times only one-third of corporate frauds are detected. We estimate that on average 10% of large publicly traded firms are committing securities fraud every year, with a 95% confidence interval of 7%-14%. Combining fraud pervasiveness with existing estimates of the costs of detected and undetected fraud, we estimate that corporate fraud destroys 1.6% of equity value each year, equal to $830 billion in 2021. |
| Keywords: | Corporate governance, Corporate fraud, Detection likelihood, Cost-beneft analysis, Securities regulation, Arthur Andersen |
| JEL: | G30 G34 K22 M40 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:cbscwp:327&r=fmk |
| By: | Olkhov, Victor |
| Abstract: | We show how time-series of random market trade values and volumes completely describe stochasticity of stock returns. We derive equation that links up returns with current and past trade values and show how statistical moments of the trade values and volumes determine statistical moments of stock returns. We estimate statistical moments of the trade values and volumes by the conventional frequency-based probability. However we believe that frequencies of stock returns don’t define its probabilities as market and financial concepts. We present the market-based treatment of the probability of stock returns that defines average returns during “trading day” that completely match conventional notion of the weighted value return of the portfolio. We derive how statistical moments of the market trade values and volumes define approximations of the characteristic functions and probability density functions of stock returns. We derive volatility of stock returns, autocorrelations of stock returns, returns-volume and returns-price correlations through corresponding relations between statistical moments of the market trade values and volumes. The market-based probability of stock returns reveals direct dependence of statistical properties of stock returns on market trade randomness and economic uncertainty. Any reasonable forecasting of stock returns should be based on well-grounded predictions of the market trades and economic environment. |
| Keywords: | stock returns; volatility; correlations; probability; market trades |
| JEL: | C00 D40 E43 E50 G00 G12 G15 |
| Date: | 2023–02–06 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:116234&r=fmk |
| By: | Severin Bernhard; Philip Vermeulen |
| Abstract: | Using high-frequency identification, we investigate leverage of the firm and economy-wide leverage as determinants of the sensitivity of a firm's stock price to monetary policy announcements. We show that the effect of economy-wide leverage is substantially larger than the effect of the firm's own leverage. It is sufficient for the response of a firm's stock price to strengthen that other firms in the economy become more leveraged. We further show that economy-wide leverage fluctuations explain the time-varying effects of monetary policy on stock prices. Our results are robust controlling for a variety of common business cycle variables and household leverage. |
| Keywords: | Monetary policy, stock returns, leverage |
| JEL: | E44 E52 G14 |
| Date: | 2023–01 |
| URL: | http://d.repec.org/n?u=RePEc:een:camaaa:2023-07&r=fmk |
| By: | Christoph Görtz; Plutarchos Sakellaris; John D. Tsoukalas |
| Abstract: | We study how firms adjust their financial positions around the times when they undertake lumpy adjustments in capital or employment. Using U.S. firm level data, we document systematic patterns of cash and debt financing around lumpy adjustment, remarkably similar across capital and employment. Firm-specific fundamentals reflected in Tobin’s Q, profitability and productivity are leading indicators of the lumpy adjustment. Cash and debt capacity are actively manipulated, and contribute significantly quantitatively, to increase financial resources in anticipation of the expansion of firm capacity. Lumpy contractions in productive capacity follow years where firms reduce cash balances and hold above average levels of debt. During and after contractions, firms rebuild cash and reduce debt growth significantly in a concerted effort to restore financial resources by adjusting their productive operations. |
| Keywords: | Lumpy Adjustment, Firm Capital and Employment Dynamics, Leverage, Debt, Cash |
| JEL: | G30 G32 E32 |
| Date: | 2023–01 |
| URL: | http://d.repec.org/n?u=RePEc:een:camaaa:2023-04&r=fmk |
| By: | Kazuki Amagai; Tomoya Suzuki |
| Abstract: | In the practical business of asset management by investment trusts and the like, the general practice is to manage over the medium to long term owing to the burden of operations and increase in transaction costs with the increase in turnover ratio. However, when machine learning is used to construct a management model, the number of learning data decreases with the increase in the long-term time scale; this causes a decline in the learning precision. Accordingly, in this study, data augmentation was applied by the combined use of not only the time scales of the target tasks but also the learning data of shorter term time scales, demonstrating that degradation of the generalization performance can be inhibited even if the target tasks of machine learning have long-term time scales. Moreover, as an illustration of how this data augmentation can be applied, we conducted portfolio management in which machine learning of a multifactor model was done by an autoencoder and mispricing was used from the estimated theoretical values. The effectiveness could be confirmed in not only the stock market but also the FX market, and a general-purpose management model could be constructed in various financial markets. |
| Date: | 2023–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2301.12346&r=fmk |
| By: | Jaros{\l}aw Gruszka; Janusz Szwabi\'nski |
| Abstract: | The debate between active and passive investment strategies has been ongoing for many years and is far from being over. In this paper, we show that the choice of an optimal portfolio management strategy depends on an investment climate, which we measure via the parameters of the Heston model calibrated to the real stock market data. Depending on the values of those parameters, the passive strategy may namely outperform the active ones or vice versa. The method is tested on three stock market indices: S\&P500, DAX and WIG20. |
| Date: | 2023–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2302.01816&r=fmk |
| By: | Jumbe, George |
| Abstract: | Credit risk, also known as default risk, is the likelihood of a corporation losing money if a business partner defaults. If the liabilities are not met under the terms of the contract, the firm may default, resulting in the loss of the company. There is no clear way to distinguish between organizations that will default and those that will not prior to default. We can only make probabilistic estimations of the risk of default at best. There are two types of credit risk default models in this regard: structural and reduced form models. Structural models are used to calculate the likelihood of a company defaulting based on its assets and liabilities. If the market worth of a company's assets is less than the debt it owes, it will default. Reduced form models often assume an external cause of default, such as a Poisson jump process, which is driven by a stochastic process. They model default as a random event with no regard for the balance sheet of the company. This paper provides a Review of credit risk default models. |
| Date: | 2023–01–13 |
| URL: | http://d.repec.org/n?u=RePEc:osf:osfxxx:ksb8n&r=fmk |
| By: | Kuntal K Das; Logan J Donald; Alfred V Guender |
| Abstract: | This paper presents a model of alternative sources of credit – bank vs. bond finance - to examine the credit substitution hypothesis. Our framework produces testable hypotheses about the behaviour of price- and quantity-based information variables. Examining data from ten Euro-area countries, we find that a credit spread outperforms a finance mix as a predictor of economic activity in both time series and pooled data regressions. There are clear signs of asymmetric and maturity effects in the data. Positive changes in the credit spread predict decreases in economic activity while negative changes bear no informative content. The asymmetric effect is exceptionally strong in pooled data and is present in short-term, long-term, and total credit spreads. In country-specific time-series regressions the asymmetric signalling property is strongest for the long-term credit spread. By contrast, we find no substantive evidence that changes in a quantity-based finance mix have robust predictive power. |
| Keywords: | Credit spread, finance mix, predictive ability, asymmetric effects, maturity split |
| JEL: | E3 E4 G1 |
| Date: | 2023–02 |
| URL: | http://d.repec.org/n?u=RePEc:een:camaaa:2023-08&r=fmk |
| By: | Valseth, Siri (University of Stavanger) |
| Abstract: | This paper studies the drivers of primary dealers’repo activity and the e¤ect of repo market frictions on bond market liquidity. It separates the two tiers of the bond market, the electronic limit order book (LOB) and the over-the-counter (OTC) market. The results, based on dealer-specific repo quantities and cash market trades in Norwegian government bonds, show that the passive order flow in the LOB is an important driver of repo activity. Liquidity in both tiers deteriorate with higher repo specialness, which represents the cost of "borrowing" bonds. This suggests that primary dealers enter the repo market to borrow bonds when their inventories are depleted via executed ask limit orders. Intermediaries in electronic bond markets thus face an additional risk related to the level of repo specialness. |
| Keywords: | Electronic bond trading; primary dealers; repo market frictions; safe asset scarcity. |
| JEL: | G12 G14 G15 G20 |
| Date: | 2023–02–10 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:stavef:2023_001&r=fmk |