Portfolio Trading Research Articles

Input–Output Linkages and Sectoral Volatility

Why are some sectors more volatile than others? This paper uncovers evidence of an empirical regularity in the US economy: upstream sectors that are far removed from final consumers have higher levels of output volatility. The relationship between volatility and upstreamness is not driven by sector size, sector concentration, trade openness or the level of aggregation at which sectors are defined. Rather, the paper shows a stronger link between upstreamness and nominal output volatility, than with indexes of real output volatility. Aggregate exports at the national level also reflect the empirical regularity of higher volatility with upstreamness: Export volatility is higher in economies with trade portfolios dominated by upstream sectors. On average, reducing the upstreamness of exports by 1 also reduces aggregate export volatility by about 10%. The pattern of higher volatility for upstream sectors is explained with a model of demand shock transmission between sectors.

Input-Output Linkages and Sectoral Volatility

Why are some sectors more volatile than others? This paper uncovers evidence of an empirical regularity in the U.S. economy: upstream sectors that are far removed from final consumers have higher levels of output volatility. The relationship between volatility and upstreamness is not driven by sector size, sector concentration, trade openness or the level of aggregation at which sectors are defined. Rather, the paper shows a stronger link between upstreamness and nominal output volatility, than with indexes of real output volatility. Aggregate exports at the national level also reflect the empirical regularity of higher volatility with upstreamness: Export volatility is higher in economies with trade portfolios dominated by upstream sectors. On average, reducing the upstreamness of exports by one, also reduces aggregate export volatility by about 10%. The pattern of higher volatility for upstream sectors is explained with a model of demand shock transmission between sectors.

The Rise and Fall of the Carry Trade: Links to Exchange Rate Predictability

We establish the out-of-sample predictability of monthly exchange rates via machine learning techniques based on 70 predictors capturing country characteristics, global variables, and their interactions. To better guard against overfitting in our high-dimensional and noisy data environment, we make additional adjustments to “off-the-shelf” implementations of machine learning techniques, including imposing economic constraints. The resulting forecasts consistently outperform the no-change benchmark, which has proven difficult to beat. Country characteristics are important for forecasting, once they interact with global variables. Machine learning forecasts also markedly improve the performance of a carry trade portfolio, especially since the global financial crisis.

The environmental and socioeconomic trade-offs of importing crops to meet domestic food demand in China

China increasingly relies on agricultural imports, driven by its rising population and income, as well as dietary shifts. International trade offers an opportunity to relieve pressures on resource depletion and pollution, such as nitrogen (N) pollution, while it poses multiple socioeconomic challenges, such as food availability. To quantify such trade-offs considering the roles of different crop types, we developed a unique crop-specific N budget database and assessed the impacts of the crop trade on multiple sustainability concerns including N pollution caused by crop production, crop land area, independence of food supply, and trade expenditures. We quantified the ‘virtual’ N inputs and harvested areas, which are the amount of N inputs and land resources used in exporting countries for China’s crop import. In addition, we proposed the concepts of ‘alternative’ N inputs and harvested area to quantify the resources needed if imported crops were produced in China. By comparing results from ‘alternative’ and ‘virtual’ concepts, we assessed the role of trade in Chinese crops over the past 30 years (i.e. 1986–2015) in alleviating N pollution and saving cropland in China and the world. Crop imports accounted for 31% of Chinese crop N consumption in 2015, and these crop imports eased the need for an additional cropland area of 62 million ha. It also avoided an N surplus by 56 and 36 Tg (Tg = 109 kg) for China and the world respectively but led to $621 billion crop trade expenditures over the 30 year period. The N pollution damage avoided by crop imports in economic terms was priced at $22 ± 16 billion in 2015, which is lower than the crop trade expenditures but may be surpassed in the future with the development of the Chinese economy. Optimizing a crop trade portfolio can shift domestic production from N-intensive crop production (e.g. maize, fruits, and vegetables) to N-efficient crop production (e.g. soybeans), and consequently mitigate an N surplus by up to 12%. Improving N use efficiency for individual crops can further increase the mitigation potential of N surplus to 30%–50%, but requires technology advancement and policy incentives.

Dynamic risk-sensitive fuzzy asset management with coherent risk measures derived from decision maker’s utility

A dynamic risk-sensitive portfolio optimization problem under risk constraints is discussed by the use of coherent risk measures and fuzzy random variables. Risk-sensitive expected rewards under utility functions are approximated by weighted average value-at-risks, and risk constraints are described by coherent risk measures. The coherent risk measures are represented as weighted average value-at-risks with the best risk spectrum derived from decision maker’s risk-averse utility, and the risk spectrum can inherit the risk-averse property of the decision maker’s utility as weighting. By perception-based extension, the risk-sensitive estimation and coherent risk measures are applied to fuzzy random variables. To find feasible regions of risk constraints, a one-step risk-minimizing problem is investigated by mathematical programming. Next, dynamic risk-sensitive total reward maximization under the feasible coherent risk constraints is discussed by dynamic programming. A few numerical examples are investigated to explain the significance of the obtained results. The proposed method is extremely effective for high-speed portfolio trading.

Multiportfolio optimization with CVaR risk measure

In the financial industry, the trading of multiple portfolios is usually aggregated and optimized simultaneously. When multiple portfolios are managed together, unique issues such as market impact costs must be dealt with properly. Conditional Value-at-Risk (CVaR) is a coherent risk measure with the computationally friendly feature of convexity. In this study, we propose the new combination of CVaR with the multiportfolio optimization (MPO) problem and develop optimization models with using CVaR to measure risks in MPO problems. A five-step scheme is presented for practical operations with considering the impact costs caused by aggregating the trading of multiple portfolios. The impact of CVaR on returns and utility in MPO environment is studied, and the comparisons with existing methods and sensitivity analysis are reported.

Algo-Trading Strategy for Intraweek Foreign Exchange Speculation Based on Random Forest and Probit Regression

In the Forex market, the price of the currencies increases and decreases rapidly based on many economic and political factors such as commercial balance, the growth index, the inflation rate, and the employment indicators. Having a good strategy to buy and sell can make a profit from the above changes. A successful strategy in Forex should take into consideration the relation between benefits and risks. In this work, we propose an intraweek foreign exchange speculation strategy for currency markets based on a combination of technical indicators. This system has a two-level decision and is composed of the Probit regression model and rules discovery using Random Forest. There are two minimum requirements for a trading strategy: a rule to enter the market and a rule to exit it. Our proposed system, to enter the currency market, should validate two conditions. First, it should validate Random Forest access rules over the following week while in the second one the predicted value of the next day using Probit should be positive. To exit the currency market just one negative warning from Probit or Random Forest is enough. This system was used to develop dynamic portfolio trading systems. The profitability of the model was examined for USD/(EUR, JYN, BRP) variation within the period from January 2014 to January 2016. The proposed system allows improving the prediction accuracy. This indicates a good prediction of the behavior market and it helps to identify the good times to enter it or to leave it.

Practical Applications of Carry On

<h3>Practical Applications Summary</h3> A frequent forward rate bias in foreign exchange markets enabled profits from carry trades for decades. Flying in the face of the efficient market hypothesis, this fact is generally regarded as a puzzle in finance. Yet since the global financial crisis, profitable carry trades are no longer prevalent. Past “abnormal” performance may have been compensation for risk rather than an anomaly. By focusing on the riskiest currency pairs, carry may continue to be a profitable strategy going forward. In <b>Carry On</b>, published in the Fall 2019 issue of <b><i>The Journal of Alternative Investments</i></b>, <b>Megan Czasonis</b>, <b>Baykan Pamir</b>, and <b>David Turkington</b> (all of <b>State Street Associates</b>) explore what factors and circumstances favor a profitable carry trade and whether it can be a viable strategy going forward. They examine interest rate differentials, valuations, crowding, and volatility in relation to the performance of an FX carry trade portfolio. Their results provide some new pieces to the long-standing puzzle and valuable insights for investors. <b>TOPICS:</b>Currency, global, financial crises and financial market history, performance measurement

Forward-Looking Policy Rules and Currency Premia

We evaluate the cross-sectional predictive ability of a forward-looking monetary policy reaction function, or Taylor rule, in both statistical and economic terms. We find that investors require a premium for holding currency portfolios with high implied interest rates while currency portfolios with low implied rates offer negative currency excess returns. Our forward- looking Taylor rule signals are orthogonal to current nominal interest rates and disconnected from carry trade portfolios and other currency investment strategies. The profitability of the Taylor rule portfolio spread is mainly driven by inflation forecasts rather than the output gap and is robust to data snooping and a wide range of robustness checks.

WHAT DRIVES INVESTMENT FLOWS INTO SOCIAL TRADING PORTFOLIOS?

AbstractWe investigate investment flows into more than 5,300 social trading portfolios that are issued as structured products and are tradable at a regular exchange. We find that investment flows chase past performance. However, in contrast to mutual fund flows, the flow–performance relation exists nearly exclusively for the best performing social trading portfolios. Flows follow raw returns rather than factor model alphas. Additionally, flows are highly persistent. Finally, social trading portfolios with higher visibility on the web page of the social trading platform as well as traders communicating actively to investors via public comments attract higher inflows.

Dynamic portfolio allocation in goals-based wealth management

We report a dynamic programming algorithm which, given a set of efficient (or even inefficient) portfolios, constructs an optimal portfolio trading strategy that maximizes the probability of attaining an investor’s specified target wealth at the end of a designated time horizon. Our algorithm also accommodates periodic infusions or withdrawals of cash with no degradation to the dynamic portfolio’s performance or runtime. We explore the sensitivity of the terminal wealth distribution to restricting the segment of the efficient frontier available to the investor. Since our algorithm’s optimal strategy can be on the efficient frontier and is driven by an investor’s wealth and goals, it soundly beats the performance of target date funds in attaining investors’ goals. These optimal goals-based wealth management strategies are useful for independent financial advisors to implement behavioral-based FinTech offerings and for robo-advisors.

Carry trades and commodity risk factors

This paper investigates the importance of commodity prices for the returns of currency carry trade portfolios. We adopt a recently developed empirical factor model to capture commodity commonalities and heterogeneity. Agricultural material and metal price risk factors are found to have explanatory power on the cross-section of currency returns, while commodity common and oil factors do not. Although stock market risk is strongly linked to currencies in developed countries, the agricultural material factor is more important for emerging currencies compared to the stock market factor. This suggests that emerging currencies are somewhat segmented from a common financial market shock.

A constrained portfolio trading system using particle swarm algorithm and recurrent reinforcement learning

This study extends a recurrent reinforcement portfolio allocation and rebalancing management system with complex portfolio constraints using particle swarm algorithms. In particular, we propose to use a combination of recurrent reinforcement learning (RRL) and particle swarm algorithm (PSO) with Calmar ratio for both asset allocation and constraint optimization. Using S&P100 index stocks, we show such a system with a Calmar ratio based objective function yields a better efficient frontier than the Sharpe ratio and mean-variance based portfolios. By comparing with multiple PSO based long only constrained portfolios, we propose an optimal portfolio trading system that is capable of generating both long and short signals and handling the common portfolio constraints. We further develop an adaptive RRL-PSO portfolio rebalancing decision system with a market condition stop-loss retraining mechanism, and we show that the proposed portfolio trading system outperforms the benchmarks consistently especially under high transaction cost conditions.

Attention Private Equity and Venture Capitalists: Do You Know How to Manage Investment Portfolio Trade Risks?

In today’s trade policy environment, it seems as if there is a new business risk every day. One day, it is sanctions on Chinese entities for doing business in North Korea; the next day, it is the activities of Russian oligarchs. Then companies are hit with four waves of increased import duties on steel, aluminum, and most raw materials and finished products from China. Even if firms are not importing on their own, US manufacturers are receiving “the letter” stating that they must pay additional fees on parts and other inputs. Sometimes the affected products simply involve imported packaging or aluminum can made in America from imported raw material. With so much change and uncertainty, how do investors identify and evaluate these and other trade-related risks that may impact their portfolio companies? TOPICS:Private equity, risk management, analysis of individual factors/risk premia, legal/regulatory/public policy

A minimal simplified model for assessing and devising global LNG equilibrium trade portfolios while maximizing energy security

The global environmental concerns associated with the increased energy demand have promoted the use of natural gas as a low-carbon fuel. This caused an increase in LNG supply and demand coupled with the need for useful tools to devise secure LNG trade schemes. While most studies focused either on assessing the security of past/current LNG trade, or on extrapolating LNG trade trends in the future, this study introduces a new simplified coarse-grained model that devises optimized LNG import/export schemes ensuring secure trade for suppliers and markets. The model is based only on four variables: LNG demand, LNG liquefaction capacity, utilization of liquefaction capacity, and transport distance. First, the model was used to generate LNG trade portfolios for Asia Pacific and Europe between 2003 and 2016. The HHI index then showed that the model always devised a more diversified, secure LNG trade. The different import strategies of the two markets and their evolution with time was highlighted, revealing more secure import regulations by AP. Finally, the model was used to forecast LNG import portfolios for AP and EU in 2030, which emphasized a significant change in the market share of conventional exporters with the introduction of new US and Australian LNG.

Determinants of Ecotourism Trade in Cameroon

Trade in services accounts for a large and growing share of both production and employment in Cameroon. Services have been among the fastest-growing components of Cameroon’s trade portfolio in sectors like telecommunications, information and communication technology, finance, transport and construction. Cameroon is endowed with potentials in the provision of services such as tourism, particularly ecotourism. However, the development of ecotourism still appears to be in its infancy. The country is still short of the objective to qualify as a developed tourism destination by the World Tourism Organisation standards. This raises important policy research questions. This paper examines the ecological assets and investigates the determinants of ecotourism in Cameroon; using an annual panel data of both primary and secondary data to analyse the economic, social, psychological and political impacts of ecotourism on local communities. The results revealed that the income of the origin countries, the price and the supply of available amenities in Cameroon significantly affect the demand for ecotourism services from Cameroon. The paper concludes that ecotourism has double-barrel potential to promote trade growth and enhance the welfare of local communities.

Carry Trades and Commodity Risk Factors

This paper investigates the importance of commodity prices for the returns of currency carry trade portfolios. We adopt a recently developed empirical factor model to capture commodity commonalities and heterogeneity. Agricultural material and metal price risk factors are found to have explanatory power on the cross-section of currency returns, while commodity common and oil factors do not. Although stock market risk is strongly linked to currencies in developed countries, the agricultural material factor is more important for emerging currencies compared to the stock market factor. This suggests that emerging currencies are somewhat segmented from a common financial market shock.

The effects of diesel price on regional trade in the USA

Abstract The abundant trade literature offers little insight on how diesel prices affect intranational trade. We fill this gap by calibrating a structural gravity model to recent U.S. interstate trade data. We discover that, for any distance, the elasticity of trade to diesel prices is much greater for low-valued commodities than for high-valued commodities. The general equilibrium result shows that a nationwide diesel price increase leads to heterogeneous decreases in trade across distances from the exporting state because of adjustments in multilateral costs and trade portfolio. Moreover, it increases the share of high-valued commodities in interstate trade.

COMMENTARY: A Market Structure That Fits the Needs of Portfolio Managers

Trading “these” securities for “those” (portfolio trades) can be expensive if done through our current continuous markets. This article compares a broker-implemented blind bid solution to this problem in a continuous market setting versus a combined value computerized call market that maximizes available liquidity to create balanced trades between such lists. The technology is known: combined value markets are in use today servicing markets in logistics contracts, emissions permits, spectrum licenses, and aerospace procurement. Should not financial concerns, such as custodial banks, be currently offering such services to their clients?

Heterogeneous Institutional Preferences and Informativeness: Evidence From China

Using complete semi-annual stock holdings of seven different types of institutional investors: mutual funds, insurance companies, qualified foreign institutional investors (QFIIs), social security funds, proprietary trading portfolio of securities companies, asset management portfolio of securities companies, and trust funds, we investigate those heterogeneous institutional investors’ preferences and informativeness in the China’s A-shares market from 2005 to 2017. We find that although those institutional investors in aggregate prefer to hold growth, larger size, higher share price, lower turnover, lower volatility, higher leverage, and past winner stocks, insurance companies and trust funds like to hold value and past loser stocks, suggesting that insurance companies and trust funds are more conservative than other institutions. In addition, we find that stock holdings of QFIIs and holding changes of mutual funds and social security funds account for the explanatory power of total institutional ownership on stock returns in the following six months, suggesting that mutual funds, QFIIs, and social security funds are more informed than other institutions. Furthermore, our trading portfolio analysis shows that social security funds demonstrate a superior stock picking ability while insurance companies’ trading portfolio exhibits a loss in the following six months. Collectively, because social security funds have a higher than average churn rate and insurance companies exhibit a lower than average churn rate during our sample period, our evidence suggests that more informed institutions also trade more often in the China’s A-shares market.