Search Results for « Optimal ETF Selection »

The problem of optimal allocation of voltage-reactive regulatory resources has been widely considered in the field of planning and management of the power systems in order to maintain the voltage profile, as one of the most important aspects of the operation of a network, within the given limits. During the installation of these devices, the topology of the network is changed, which is why it is essential to pay sufficient attention to the sizing of these devices in the planning phase, from the point of view of selecting adequate power, and to choose suitable locations for their location.This paper proposes a method for determining the minimum required number of reactive power compensation devices, their locations and powers, with the aim of improving the voltage profile. The difference between this work in comparison to existing research work is that the number of devices, as well as their locations, will not be defined in advance, so that the locations and power of the devices will be treated as variables. The effectiveness of the proposed method will be tested using IEEE test systems.

A U.S. investor holding a large, low-basis single stock can move into a diversified fund without triggering the embedded capital-gains tax by contributing securities in kind under Internal Revenue Code Section 351, provided the basket she contributes clears a statutory diversification test: no single issuer above 25% of its value, and the five largest issuers together at or below 50%. The regulation tests each contributor's own basket, so the other contributors cannot rescue a concentrated investor. This note formalizes the seeding decision the test creates and quantifies its economics. We write the 25%/50% test as linear constraints on contribution weights, cast the choice of seed basket as a linear program that maximizes deferred gain, and derive three closed-form consequences of the caps. Contributing a position of value C at the 25% ceiling requires at least 3C of eligible ballast from the investor's own holdings, so the share of a concentrated position that can go in is her eligible ballast divided by 3C; a seed that places one issuer at the cap can be no more diversified than sixteen effective names; and deferral loses to an immediate sale only if the capital-gains rate at the horizon exceeds a breakeven set by today's rate, the growth factor and the step-up probability alone. We then compare four ways to handle a $10M low-basis position (351 seed, exchange fund, hold-and-borrow, and sell-and-pay) on after-tax terminal wealth and on a risk-aversion-adjusted certainty equivalent, over horizons of 10 to 30 years and across the probability of a basis step-up at death. In the base calibration the 351 seed has the highest certainty equivalent at every horizon, because it pairs the deferral of a sale with real diversification at a low fee. Hold-and-borrow posts the highest expected wealth and the lowest certainty equivalent, as borrowing against an undiversified position would predict. All figures are reproducible from a single script with a fixed seed.

<p>Better forecasts do not necessarily produce a better global equity portfolio. The question is whether those signals improve the final portfolio once benchmark-relative risk, overlapping holdings, trading, and costs are taken into account. This paper examines a global equity ETF portfolio that combines machine-learning selection, benchmark-aware regional allocation, and a small allocation across themes, sectors, and styles. The components are evaluated together against the iShares MSCI ACWI ETF (ACWI).</p> <p>The common daily evaluation covers 31 January 2019 through 31 August 2026. In this reconstructed research history, the portfolio compounded at 18.9% annually versus 13.8% for ACWI, with 6.15% tracking error, a 0.819 information ratio, and a maximum relative drawdown of 8.10%. The advantage was uneven: the portfolio lagged ACWI in 2022, rolling active returns turned negative in some periods, and primary selection alone had a better recent trade-off between active return and tracking error than the complete portfolio.</p> <p>Longer component histories include additional market environments but are not a longer backtest of the integrated portfolio. Because the model combination was selected during research, the evidence does not establish an optimal mix. The relevant test is what each component contributes after the portfolios are combined.</p>

A high liquidity, low expense ratio and the possibility to conduct arbitrage allow exchange-traded funds (ETFs) to be used for short sales. Bearish investors can also buy inverse ETFs. This paper aims to outline two investment approaches for bearish ETF investors and the differences between these two approaches; it also aims to examine the relationship between price and an indicator of volume and evaluate the final positions in selected ETFs in selected periods. Short ETFs dominate in simplicity, flexibility, paying out dividends and especially in the limited size of the loss. On the other hand, their structure, which demands daily rebalancing, causes substantial deviation from the benchmark in the long-term and leads to a higher expense ratio, and lower liquidity increases bid-ask spreads. Negative aspects of ETF short selling lie in unlimited loss, high borrowing costs, the need for margin accounts, variability of loan fees and the possibility of a transaction recall by the lender. On the contrary, margin operations enable potentially higher appreciation of capital by generating rebate rates. Our results show that with the decrease in value of the most used ETFs, short interest is growing for those funds where there is a very strong negative correlation implying hedging tendencies. Short selling proved to be a more advantageous strategy in the observed period of market downturn, as well as in 2011–2017, due to negative returns, however, by applying margin trading inverse ETFs turned out to make less losses. Sector-oriented inverse ETFs are the exception, where the largest differences between these two strategies are recorded. However, the final conclusion of the suitability of one of the analyzed strategies depends on the market volatility and the direction of the market itself.

This study aims to optimize the ETF fund selection strategy. By combining the time-series momentum strategy and the machine learning scoring model, a systematic analysis and empirical research on the main stock ETFs and cross-border ETFs in the Chinese market are conducted. We constructed a combination strategy that combines momentum indicators and machine learning scoring, and conducted a backtest. The results show that the combination strategy is significantly better than the momentum strategy or machine learning model alone and the benchmark index in terms of annualized return, maximum drawdown and Sharpe ratio. By selecting momentum indicators (such as simple momentum, exponential moving average, relative strength index) and machine learning models (such as logistic regression, support vector machine, random forest), we verified the effectiveness of these methods in ETF fund selection. The backtest results show that combining different methods can effectively improve the performance of ETF fund selection strategies and achieve better investment results. This paper further analyzes the investment results of each strategy, and evaluates the stability and risk control ability of the strategy through cross-validation and backtesting methods. The results show that the combination strategy performs well in terms of return, risk control and risk-adjusted returns, providing investors with a scientific and effective investment tool.