Fund Prices Research Articles

S&P 500 microstructure noise components: empirical inferences from futures and ETF prices

By studying the differences between futures prices and exchange‐traded fund prices for the S&P 500 index, original results are obtained about the distribution and persistence of the microstructure noise component created by positive bid‐ask spreads and discrete price scales. The bivariate density of this component for futures and exchange‐traded fund prices is estimated from high‐frequency prices, to provide estimates of the marginal noise densities and measures of noise dependence across the markets studied. Properties of the residual microstructure noise, created by factors other than discrete prices, are also estimated. The residual component has more variation and less persistence than the discrete‐price component during the period examined, from January 2010 to December 2012.

Unlocking ETF price forecasting: Exploring the interconnections with statistical dependence-based graphs and xAI techniques

In the complex landscape of financial markets, accurately predicting Exchange-Traded Fund (ETF) price movements requires advanced methodologies. This research introduces a practical approach that integrates network analysis with graph embeddings, specifically utilizing Node2Vec, to enhance financial prediction models' performance and interpretability. By representing the intricate relationships within financial markets in a lower-dimensional space, we improve the efficiency of AI-driven predictions. A key component of our method is applying the SHAP Explainable AI (xAI) framework, which helps interpret our tree-based models' decision-making process. Using six different tree-based models, our approach delivers accurate predictions while maintaining transparency in model interpretation. This combination of graph embeddings and explainability tools enables stakeholders to understand better the factors influencing financial market behavior, improving decision-making based on AI models.

Impact of an Outpatient Word Catheter Program for Bartholin cysts and abscesses: A retrospective cohort study.

To assess the impact of an Outpatient Word Catheter Program (OWCP) on outcomes in women presenting with Bartholin cysts or abscesses (BC/BAs). . This retrospective cohort study reviewed 408 women presenting with BC/BAs to our tertiary unit from 2017-2022. Analysis of medical records, with subgroup analysis of pregnant patients, and comparative analysis between pre- and post-intervention groups, was completed. Financial impact analysis using national activity-based funding pricing guidance to estimate cost was conducted. Pre-intervention, 65% (n = 34) of procedures were completed in theater, but after the introduction of OWCP, 61% (n = 213) of cases were treated in the day ward (χ2 = 67.43, P <0.001). Similarly, inpatient admissions reduced; 94.2% (n = 49) pre-intervention versus 26% (n = 92) post-intervention (χ2 = 92.25, P <0.001). The mean all patient admission duration decreased from 1.52 ± 0.89 days to 0.69 ± 0.59 days (P <0.001). The mean cost for those women attending in the pre-OWCP period was €4798, versus €2704 in the women who attended post-OWCP introduction (P < 0.001). After OWCP introduction, there were significant decreases in inpatient admissions, surgical procedures in theater, general anesthetic exposure, and duration of admission. Financial impact analysis revealed a significant cost reduction of ~€2100 per patient. Outpatient or day-care Word catheter programs are feasible, affordable and acceptable services to provide to women presenting withBC/BAs.

Macroeconomic news and price jumps: evidence from ETFs and LOFs in China

PurposeWe examine the relationship between macroeconomic news and fund price jumps, using high-frequency 5-min intraday data for Exchange Traded Funds (ETFs) and Listed Open-end Funds (LOFs) from 2019 to 2020.Design/methodology/approachWe utilize the non-parametric jump test known as the LM method to detect fund price jumps. In addition, we perform Logistic regression to analyze the relationship between macroeconomic news and fund price jumps. Moreover, we use multiple linear regression to explore the relationship between fund price jumps and subsequent returns.FindingsThe probability of price jumps increases by 22.56% when macroeconomic news is released. Moreover, the returns associated with news-driven price jumps display a reversal pattern, and there is an asymmetric relationship in subsequent returns following macroeconomic shocks. Specifically, funds tend to exhibit lower returns after news-driven price jumps compared to those that are not influenced by news events.Research limitations/implicationsIn today's digital age, investors have unprecedented access to a wealth of information through the Internet and various communication platforms. News and market data can be instantly accessed and disseminated, allowing for swift dissemination of information to investors worldwide. However, despite this enhanced accessibility, investors continue to exhibit overreactions or underreactions to new information.Practical implicationsMacroeconomic news release provide crucial insights into the overall health and performance of the economy. By monitoring and analyzing these indicators, investors can gain valuable information that can guide their investment decisions. Furthermore, by fostering a transparent and reliable information disclosure systems, governments can play a critical role in ensuring the stability and transparency of the funds market.Originality/valueThe paper utilizes 5-min high-frequency data from funds and incorporates a comprehensive macroeconomic news information database. These methodological choices enhance the precision and reliability of the analysis, allowing for a more nuanced understanding of the relationship between macroeconomic news releases and fund price jumps.

The Democratization of Wealth Management: Hedged Mutual Fund Blockchain Protocol

We develop several innovations to bring the best practices of traditional investment funds to the blockchain landscape. Specifically, we illustrate how: (1) fund prices can be updated regularly like mutual funds; (2) performance fees can be charged like hedge funds; (3) mutually hedged blockchain investment funds can operate with investor protection schemes, such as high water marks; and (4) measures to offset trading related slippage costs when redemptions happen. Using our concepts – and blockchain technology – traditional funds can calculate performance fees in a simplified manner and alleviate several operational issues. Blockchain can solve many problems for traditional finance, while tried and tested wealth management techniques can benefit decentralization, speeding its adoption. We provide detailed steps – including mathematical formulations and instructive pointers – to implement these ideas and discuss how our designs overcome several blockchain bottlenecks, making smart contracts smarter. We provide numerical illustrations of several scenarios related to our mechanisms.

Reward-Based Crowdfunding Versus Initial Coin Offerings

Inspired by emerging financing techniques that offer additional financing options for entrepreneurs, we establish a stylized model to compare two prevalent crowdfunding strategies: Reward-based crowdfunding and Initial Coin Offerings (ICOs). First, we highlight that the fundamental difference between reward-based crowdfunding and ICO lies in the distinct types of price distortion, resulting in different agency costs. Specifically, compared to the first-best benchmark, reward-based crowdfunding sets a lower funding price to attract more consumers to pre-order, while ICO sets a higher market price due to speculators’ revenue-sharing in the market stage. Second, we show that the financing limit (i.e., the maximum funds raised) of reward-based crowdfunding is driven by the platform traffic, whereas that of ICO is driven by the marginal production cost. Therefore, even with numerous speculators, ICO may result in a lower financing limit than reward-based crowdfunding when the platform traffic or the marginal production cost is high. Third, we explore how products’ cost structures shape entrepreneurs’ financing preferences. ICO suits intangible products with high fixed cost and low marginal production cost, while reward-based crowdfunding aligns better with products exhibiting the opposite cost structure. Finally, we extend our model to show the robustness of our findings and offer some additional insights. For example, ICO may amplify the risk of market uncertainty compared to reward-based crowdfunding; Security Token Offerings (STO) benefits from the absence of agency costs between the entrepreneur and speculators compared to ICO while it may suffer from equity loss, particularly in risky and regulated markets, etc.

Forecasting the Future Value of a Colombian Investment Fund with LSTM Recurrent Neural Networks (LSTM)

Forecasting the Future Value of a Colombian Investment Fund with LSTM Recurrent Neural Networks (LSTM)

Democracy and Markets in a Partially Globalized World: Local and Global Financial Market Responses to Elections in Developing Countries

Abstract Global market responses to elections are at the core of debates about financial globalization in developing countries. While existing research focuses on the ability of global markets to reward and punish national governments, much less is known about the role of domestic finance. I argue that domestic financial markets (1) react more strongly to elections than global markets due to excessive exposure to political risk at home and (2) lead global market responses to elections where domestic investors have an information advantage. I find support for these hypotheses using data on country fund pricing for major emerging markets between 1988 and 2015. The results show swifter and more dramatic reactions by domestic investors, which are transmitted to international markets. The findings underscore the underappreciated role of domestic investors in state-market relations under limited globalization.

The proposed mandatory swing pricing regime and the hard close requirement : Practical considerations

In November 2022, the U.S. Securities and Exchange Commission (the ‘Commission’) proposed rule amendments that would require, among other things, open-end mutual funds to implement a mandatory swing pricing regime under certain circumstances (the ‘Proposals’). To ensure that funds receive order information with sufficient time to make a swing pricing determination, the Proposals would require funds to set a hard cut-off time for the receipt of purchase and redemption orders. Currently, the vast majority of investments in open-end mutual funds are made through intermediaries and retirement plans, such that investors who submit orders to intermediaries and retirement plans before the time the fund has established for calculating its net asset value (NAV) generally receive the same day’s NAV, even if the fund receives the order information from the intermediary or retirement plan after NAV is calculated. This is how mutual fund pricing has operated in the US for over 50 years. The Proposals would upend current operations. Under the Proposals, an investor’s order would have to be received by the fund — not a financial intermediary or a retirement plan — before the cut-off time set by the fund for calculating NAV in order to be eligible for the same day’s NAV. If adopted as proposed, the Proposals would require extensive changes to mutual fund pricing systems and infrastructure in the US. In this paper, we acknowledge the arguments against the Proposals by an overwhelming majority of industry commenters and, without undercutting those arguments, we explore practical, regulatory and compliance considerations for funds seeking to better understand how the Proposals might affect their day-to-day operations.

Predicting Healthcare Mutual Fund Performance Using Deep Learning and Linear Regression

Following the COVID-19 pandemic, the healthcare sector has emerged as a resilient and profitable domain amidst market fluctuations. Consequently, investing in healthcare securities, particularly through mutual funds, has gained traction. Existing research on predicting future prices of healthcare securities has been predominantly reliant on historical trading data, limiting predictive accuracy and scope. This study aims to overcome these constraints by integrating a diverse set of twelve external factors spanning economic, industrial, and company-specific domains to enhance predictive models. Employing Long Short-Term Memory (LSTM) and Multiple Linear Regression (MLR) techniques, the study evaluates the effectiveness of this multifaceted approach. Results indicate that incorporating various influencing factors beyond historical data significantly improves price prediction accuracy. Moreover, the utilization of LSTM alongside this comprehensive dataset yields comparable predictive outcomes to those obtained solely from historical data. Thus, this study highlights the potential of leveraging diverse external factors for more robust forecasting of mutual fund prices within the healthcare sector.

Changes in the United States Dollar’s Hegemony and Macroeconomic Implications

As a global currency, the United States dollar not only relates to the flow of global capital markets and the pricing of funds, but also has an important and far-reaching impact on the global macroeconomy. There has been growing interest on the topic of how the position of the United States dollar would change and how it will be reflected in various circumstances and situations, in the last few years. This paper used data to analyze the changes in the hegemony of the USD and macroeconomic implications. With the growth of other currencies in the market nowadays, the hegemony of the USD is being questioned. A slight decline of the US dollar as a global currency has been shown in statistics. However, the United States dollar is still retaining its power and its key roles around the world, internationally as a dominant currency. The threats from other emerging currencies are not yet strong enough to change the position of the United States dollar globally from speculations.

Enhancing Exchange-Traded Fund Price Predictions: Insights from Information-Theoretic Networks and Node Embeddings.

This study presents a novel approach to predicting price fluctuations for U.S. sector index ETFs. By leveraging information-theoretic measures like mutual information and transfer entropy, we constructed threshold networks highlighting nonlinear dependencies between log returns and trading volume rate changes. We derived centrality measures and node embeddings from these networks, offering unique insights into the ETFs' dynamics. By integrating these features into gradient-boosting algorithm-based models, we significantly enhanced the predictive accuracy. Our approach offers improved forecast performance for U.S. sector index futures and adds a layer of explainability to the existing literature.

Comparing the Effects of the Nasdaq and Hang Seng on the Stock Performance of New Oriental

By the end of 2021, New Oriental declared that it would discontinue offering K-9 curricu-lum tutoring available at all its learning centers nationally. It will refocus its efforts and al-locate its resources to academic marketing strategies unrelated to K9 subject education. The goal of this article's research is to evaluate, using regression and CAPM models, the effects of the Nasdaq and Hang Seng on the returns on New Oriental stock. To determine their beta values and intercept terms, regression analysis of New Oriental stock returns, Nasdaq index returns, and Hang Seng index returns were performed. The CAPM model, which mainly investigates the correlation between projected returns and risky assets in the stock market, acts as the asset pricing model. Beta is a crucial variable. The beta coeffi-cient is a risk indicator that measures how volatile an individual stock or stock fund's price is in comparison to the overall stock market. The result is Nasdaq has a higher risk-free rate of interest and lower Beta value. Hang Seng has a lower risk-free rate of interest and higher Beta value.

App-Based Investment Platform and Investment Decision Making: A Study of Retail Investor Behavior in Indonesia

The significant growth of retail investors in Indonesia between 2020 and 2022 is partly attributable to technological advancements that have made it easier to access investments through a variety of applications. Previous studies in developing nations like Indonesia, India, and Pakistan suggested that perception biases have influence on retail investor decision making. However, given the capabilities of current investing applications like the display of historical stock/mutual fund price data, retail investors should ideally be able to make more rational investment decisions. This study employs partial least square structural equation modeling (PLS-SEM) to examine the role of overconfidence, representativeness, and loss aversion biases that affect rational investment decision making. As an application feature, historical price data serves as the moderating variable of the interaction between them. The result of this study indicates that overconfidence influences rational investment decision making in a positive and significant manner, while representativeness and loss aversion do not have a significant effect. Price historical data as application feature also does not have significant effect in moderating the relationship between them.

Implementation of Straight Term, Weighted Average, and Term Weighted Matched-Maturity Fund Transfer Pricing (FTP) on Indonesian Bank

Transfer pricing in the banking industry has a huge impact on maintaining risks and strategic decision-making for preserving competitive advantage in the market. As a best practice, the bank owns an Asset &amp; Liabilities Management (ALM) function assigned to control internal pricing, named Fund Transfer Pricing (FTP), to regulate the inter-division fund pricing between the Funding Division and the Lending Division. While the FTP implementation provisions of a bank are not regulated due to management’s appetite, the FTP calculation methods are definitively established based on several research studies. The most sophisticated method is named Match-Maturity, by reason of matching every banking product’s tenor to the yield curve and calculating them by the repayment behavior. This paper aims to study the significance of the Match-Maturity FTP (MM-FTP) method on a bank in Indonesia which currently apply the Multiple-Pool FTP (MP-FTP) method. To achieve the set objective, the study performed the FTP calculation of identical banking products including samples and all population utilizing MP-FTP and MM-FTP. The result shows 10 out of 11 groups of product and segment experienced over-charged or over-credited employing MP-FTP, which lead to inaccurate evaluation of balance sheet management and performance measurement.

Practical Applications of Private Equity Fund Pricing in Secondary Markets: Effect of Taxes and Fund Structure

Practical Applications of Private Equity Fund Pricing in Secondary Markets: Effect of Taxes and Fund Structure

Chinese Mutual Funds Performance using 4-factor Fama-French Model, Sortino and Sharpe Ratios

This paper examined the performance of the Chinese Mutual funds using the Sharpe ratio, Sortino ration and the 4-factor Fama and French model to assess the price of the mutual funds. A sample of 221 months was used (January 2003-July 2020). The results showed the MOM had the highest return with relatively lower volatility. The mutual funds; however, were found to have a lower Sharpe, and Sortino ratios an indication that they are less attractive. The 4-factor model show statistically significant coefficients, an indication that the additional three factors in the CAPM model contributes to the exploration of the asset prices.

Closed-End Fund Discounts and Economic Policy Uncertainty

This paper empirically tests the determinants of closed-end fund (CEF) prices by employing cointegration and error-correction modeling with an advanced ARDL framework. Since CEF shares generally trade at discounts to their net asset value (NAV), we modeled CEF prices, including volatility and economic policy indices along with their NAVs. The present study consists of 31 monthly frequency CEF discount data from January 1999 to April 2018 and economic policy uncertainty (EPU) with ten subindices. This paper finds evidence for cointegration in many of the series and statistically significant coefficients in the short- and long-run estimates of the included subindices.

Technical Efficiency Analysis at BUKU 1 Banks in Indonesia Using the Data Envelopment Analysis (DEA) Method Period 2016 – 2017

This study began with an observation of the financial performance of banks in assessing the efficiency of commercial banks in Indonesia in the 2016 period and found inefficiency problems in BUKU I banks based on efficiency values (BOPO) as an indicator of bank efficiency. This study is an analysis of the measurement of bank efficiency values using quantitative analysis with secondary data obtained from the balance sheet and income statement for each bank sample (DMU) for the period 2016 - 2017 using a math-ematical model (linear program) with the Non-Parametric Data Envelopment Analysis (DEA) measure-ment method oriented to Input and assume Variable Return to Scale (VRS) with a sample of 17 Bank sam-ple data during the period 2016-2017. In each DMU there are two variables namely input and output, where the input variable consists of Price of Labor (X1), Price of Fund (X2) and Price of Physical Capital (X3) while the output variable consists of Credit (Y1), Interest Income (Y2) and other Operating Income (Y3). From the results of the measurement of efficiency using the DEA method of the 17 bank samples there are 6 efficient banks while 11 other banks are inefficient. The DEA method measurement results also get an inefficient output variable projection value, from 34 inefficiencies of output variables found there are 4 inefficiencies in the given credit variable, 15 inefficiencies in the Interest Income variable and finally 15 inefficiencies in other Operating Income variables. From the measurement of the efficiency value using the DEA method, the Bank's efficiency and inefficiency values are obtained and the factors causing the Bank's inefficiency so that these factors can be used as material for the Bank's evaluation in improving the Bank's strategic planning .

Private Equity Fund Pricing in Secondary Markets: Effects of Taxes and Fund Structure

Private equity (PE) funds are growing, beginning to take the lead in capital markets. In conjunction with this growth, secondary markets for PE fund ownership interests also have grown. Recent research investigates the valuation discount that sellers of PE fund interests incur in secondary markets, and suggests this discount is driven entirely by the illiquidity inherent in PE. This article describes how the legal structure of PE funds, instrumental to funds’ existence and operations but largely ignored in prior research, can impose a tax discount in addition to an illiquidity discount in the secondary market. Thus, it extends this new and important stream of research by highlighting that illiquidity may be only one attribute driving PE fund secondary market discounts and that after-tax, rather than pretax returns, warrant consideration in these markets.