From theory to practice: Polish equity risk factors and their implementation costs

The paper applies the microstructural approach to analyze transaction costs accrued in the process of executing orders on financial exchanges. The extended classification of transaction costs on equity markets is presented. The set of challenges which arise in the analysis of a particular component of transaction costs – market impact – is outlined. It is proposed to apply R. Almgren’s market impact model to normalize observed costs. Normalized values exhibit a lower variance, and therefore, it requires a smaller sample of orders to choose the broker who provides the lowest transaction cost. As a result, the process of assessing broker service quality takes less time. Since, during the period of assessment, the investor has to use broker services to properly evaluate them, a faster process to weed out brokers who deliver higher transaction costs positively impacts investor’s financial results. As part of the larger challenge of reducing transaction costs of trading in financial equity markets, the specific case of market impact cost optimization on the basis of R. Almgren’s model was examined. The suggested methodology focuses on a specific component of market impact, which has lower variance, and could be used to weed out brokers with subpar levels of servicesfaster.

After introducing EU Regulation on short selling and certain aspects of credit default swaps, in 2012, investors holding a significant short positions are obliged to disclose it in a public manner, through the local supervisor registry. Beside the intended transparency, such a registry also have a cogent influence on behaviour of market participants. The Multimillion euros worth of short position is a strong view on underlying stock perspective, to say the least. Thus, many investors do care about the public disclosures of short positions and some of them manage their portfolios accordingly. The aim of herein paper is to provide quantitative assessment of stock performance following short position announcement and present activity of short selling investors on Polish stock market. The research hypothesis verified in this study stated that the prices of short sold stocks are exhibiting significant downward trend relative to the benchmark in a half a year period after disclosure.

Analysis of the production and transaction costs of forest carbon offset projects in the USA

This paper used artificial neural networks (ANNs) time series predictor for approximating returns of Pakistan Stock Exchange (PSX) listed 100 companies. These projected returns are then substituted into expected returns in the Markowitz’s Mean Variance (MV) portfolio Model. For comparison empirical data used is closing prices of PSX listed stocks, Karachi Inter Bank Offer Rates (KIBOR) as risk free rate and KSE-all share index as benchmark. The Portfolio returns are compared for two datasets by employing various constraints like budget, transaction costs, and turnover constraints. The value of portfolios is measured through Sharpe ratio and Information ratio. Both Sharpe and Information ratios support use of ANNs as return predictor and optimisation tool over simple MV model implemented for empirical data as well as predicted data. ANNs framework performed better in both Long and Short positions and its portfolio returns are significantly higher as compared with MV.

The beta coefficient is widely used in company valuation. According to the assumptions of the CAPM, thanks to the beta, you can set the cost of equity and thus the discount rate in income valuation models. Similarly, in the real estate market, if you want to valuate the property by income method, a discount rate should be known. This rate can be estimated by knowing the beta coefficient for property market. Unfortunately, the estimation of this coefficient is difficult, since there is no known index of such a broad market that would contain both industrial investments and all others, such as real estate investment. Therefore, the author decided to develop a method for calculating the beta in the housing market. This is the first step towards the development of the beta calculation methodology in the commercial real estate market.The aim the article is to estimate the beta coefficient for the residential property market in Poland. The author has set the hypothesis that the mean values of the beta coefficients calculated on the hedonic index on the secondary housing market are statistically significant equal to the average values of the beta coefficient for the WIG index, the broad Stock market Index on the Polish stock market. To verify the hypothesis and consequently achieve the goal of the work, the author used the data analysis followed by the use of one-way ANOVA.The development of a beta counting methodology is the original contributor's contribution to science. In the Polish market there is no adequate method and asset appraisers apply the beta directly taken from the stock market. In addition, this work is the first phase of research that will lead to the development of a methodology to calculate the beta coefficient in the commercial real estate market.

The net value-added of an investment strategy has two parts: the expected value of the alpha skill, and the cost of implementation. The first part is positive and depends on the efficacy of a return forecasting model; the second is negative and depends on portfolio turnover and trading skills. The higher the former, and the lower the latter, the happier the investor. Typically, managers give little consideration to turnover, but throw an alpha model into an optimizer, setting turnover constraints to handle transaction costs. The explicit framework here instead integrates alpha models with portfolio turnover and transaction costs. The result allows evaluation of factors in terms of net return and then optimization of multiple factors to achieve the best net information ratio. Optimal multifactor models change as portfolio turnover constraints change, providing a potential solution for model evolution as assets under management grow.

American options are studied in a general discrete market in the presence of proportional transaction costs, modelled as bid-ask spreads. Pricing algorithms and constructions of hedging strategies, stopping times and martingale representations are presented for short (seller’s) and long (buyer’s) positions in an American option with an arbitrary payoff. This general approach extends the special cases considered in the literature concerned primarily with computing the prices of American puts under transaction costs by relaxing any restrictions on the form of the payoff, the magnitude of the transaction costs or the discrete market model itself. The largely unexplored case of pricing, hedging and stopping for the American option buyer under transaction costs is also covered. The pricing algorithms are computationally efficient, growing only polynomially with the number of time steps in a recombinant tree model. The stopping times realising the ask (seller’s) and bid (buyer’s) option prices can differ from one another. The former is generally a so-called mixed (randomised) stopping time, whereas the latter is always a pure (ordinary) stopping time.

Asymmetric Information and Volatility Forecasting in Commodity Futures Markets

Portfolio replication problem is to optimize the portfolio such that its proportion-weighted combination is the same as the given benchmark portfolio. However, the benchmark portfolio generally opens only the return to the public but other information such as the assets included in the portfolio, the proportion-weighted combination, the rebalancing date and the investment strategies is closed to the public. In order to optimize such portfolios, we propose an optimization method based on the probabilistic model-building GA in this paper. On the other hand, we are focusing on the long-short portfolio optimization. The long-short portfolio consists of the assets with long positions in which they have bought and been held and with short positions in which they have been borrowed and sold. While applying any optimization method to the long-short portfolios, the portfolio as a feasible solution must be satisfied an equality constraint. In order to make the feasible solutions effectively, we propose two techniques and then apply them to our optimization method. In the numerical experiments, we show that our method has better ability to replicate the long-short portfolios with good fitness values. We found that, however, some portfolios were not replicated though our method worked well. We also discuss this problem in this paper.

Many long-only investment managers are looking for ways to offer 130/30 strategies and other “enhanced long strategies” or “constrained long-short portfolios.” Conservative portfolio managers with little or no experience in short-selling can use sector evaluations from their firms9 investment process to add alpha with short positions in exchange-traded funds (ETFs). While individual stock selection has the greatest potential to add value on the short side as well as on the long, differences in performance among various domestic sectors come in a close second. The dispersion in sector performance combined with some advantages of using ETFs to implement short sector positions makes sector ETFs to establish the short side of a 130/30 position a compelling choice for investment managers without fully integrated long-short stock portfolios. Some sector ETFs are easy to trade, easy to borrow, and easy to integrate as the short side of a long-short portfolio. <b>TOPICS:</b>Wealth management, exchange-traded funds and applications, equity portfolio management

This thesis deals with optimal algorithms for trading of financial securities. It is divided into four parts: risk-averse execution with market impact, Bayesian adaptive trading with price appreciation, multiperiod portfolio selection, and the generic online search problem k-search. Risk-averse execution with market impact. We consider the execution of portfolio transactions in a trading model with market impact. For an institutional investor, especially in equity markets, the size of his buy or sell order is often larger than the market can immediately supply or absorb, and his trading will move the price (market impact). His order must be worked across some period of time, exposing him to price volatility. The investor needs to find a trade-off between the market impact costs of rapid execution and the market risk of slow execution. In a mean-variance framework, an optimal execution strategy minimizes variance for a specified maximum level of expected cost, or conversely. In this setup, Almgren and Chriss (2000) give path-independent (also called static) execution algorithms: their trade-schedules are deterministic and do not modify the execution speed in response to price motions during trading. We show that the static execution strategies of Almgren and Chriss (2000) can be significantly improved by adaptive trading. We first illustrate this by constructing strategies that update exactly once during trading: at some intermediary time they may readjust in response to the stock price movement up to that moment. We show that such single-update strategies yield lower expected cost for the same level of variance than the static trajectories of Almgren and Chriss (2000), or lower variance for the same expected cost. Extending this first result, we then show how optimal dynamic strategies can be computed to any desired degree of precision with a suitable application of the dynamic programming principle. In this technique the control variables are not only the shares traded at each time step, but also the maximum expected cost for the remainder of the program; the value function is the variance of the remaining program. This technique reduces the determination of optimal dynamic strategies to a series of single-period convex constrained optimization problems. The resulting adaptive trading strategies are “aggressive-in-the-money”: they accelerate the execution when the price moves in the trader’s favor, spending parts of the trading gains to reduce risk. The relative improvement over static trade schedules is larger for large initial positions, expressed in terms of a new nondimensional parameter, the market power μ. For small portfolios, μ → 0, optimal adaptive trade schedules coincide with the static trade schedules of Almgren and Chriss (2000). Bayesian adaptive trading with price appreciation. This part deals with another major driving factor of transaction costs for institutional investors, namely price appreciation (price trend) during the time of a buy (or sell) program. An investor wants to buy (sell) a stock before other market participants trade the same direction and push up (respectively, down) the price.

We use an innovative practitioner technique to investigate the interplay between the ex post performance of momentum strategies and transaction costs, rebalancing frequency, turnover constraints, and fund size. We have three interrelated main results: first, the level of and correlation between active returns to price momentum and earnings momentum strategies vary dramatically with these factors; second, strategies that are fearful of ex ante transaction costs generate returns net of transaction costs that are far superior to the net returns of naive strategies; and third, obtaining better traction with the unique elements of each strategy yields a more profitable combined strategy.

The article substantiates the origin of transaction costs of stock trading from the point of view of the microstructure of the stock market. The article is devoted to the problem of improving the efficiency of decision-making in the field of portfolio investment, in particular in terms of reducing the cost of conducting operations. The object is the microstructure of the stock market, which allows to study in detail the process of making transactions with securities. The origin of transaction costs of stock trading was determined from the point of view of the microstructure of the market: as an appropriate fee for urgency and certainty in the purchase and sale process; as the premium for participation in market trading and conducting the exchange to stimulate potential counterparties; as the result of potential discrepancy in terms of information that the participants of exchange operations have. The classification of transaction costs is systematized according to various criteria: clarity of identification and assessment (explicit – commissions of intermediaries and the stock exchange, taxes, etc.and implicit – costs of spread, timeliness, market impact, missed opportunities); according to the reason for the various transaction costs (taxes, commissions, transaction execution costs and costs of missed opportunities). It is established that implicit costs add a significant level of uncertainty to the investment decision-making process, and therefore their evaluation and control is a priority issue for the investment entity. The main approaches to quantification of implicit transaction costs – missed opportunity costs, spread costs, and market impact costs-are summarized. Prospects for improving the tools for estimating transaction costs of operations in organized stock markets are determined.

Recent studies find evidence that small funds outperform large funds. This fund size effect is commonly hypothesized to be caused by transaction costs. Due to the lack of transactions data, prior studies have investigated the transaction costs theory indirectly. Our study, however, analyzes the daily transactions of active Australian equity managers and finds aggregate market impact costs incurred by large managers are significantly greater than that incurred by small managers. Furthermore, we show large managers exhibit preferences for trade package formation and portfolio characteristics consistent with transaction cost intimidation. We analyze the interaction between transaction cost intimidation and the fund size effect, and document that large managers pursuing a highly active trading strategy suffer more from fund size, than large funds following a more passive strategy. This suggests the fund size effect is related to transaction costs, as trading activity is a good proxy for expected market impact. Finally, based on a simulation experiment, we find that transaction cost intimidation is at least as important as the increase in market impact costs due to fund size.

Recent studies find evidence that small funds outperform large funds. This fund size effect is commonly hypothesized to be caused by transaction costs. Due to the lack of transactions data, prior studies have investigated the transaction costs theory indirectly. Our study, however, analyses the daily transactions of active Australian equity managers and finds aggregate market impact costs incurred by large managers are significantly greater than those incurred by small managers. Furthermore, we show large managers exhibit preferences for trade package formation and portfolio characteristics consistent with transaction cost intimidation. We analyse the interaction between transaction cost intimidation and the fund size effect, and document that large managers pursuing a highly active trading strategy suffer more from fund size, than large funds following a more passive strategy. This suggests the fund size effect is related to transaction costs, as trading activity is a good proxy for expected market impact. Finally, based on a simulation experiment, we find that transaction cost intimidation is at least as important as the increase in market impact costs due to fund size.