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Daily signals, sent at 09:00 EST (15:00 CET) - Build robust strategies with low beta - Universe: S&P500

Strategy: - Selection of top 10 long stocks and top 10 short stocks

United States New York Stock Exchange: Index: MSCI US Sector Neutral Quality Index data was reported at 5,456.459 NA in Apr 2025. This records a decrease from the previous number of 5,508.026 NA for Mar 2025. United States New York Stock Exchange: Index: MSCI US Sector Neutral Quality Index data is updated monthly, averaging 2,704.364 NA from Jan 2012 (Median) to Apr 2025, with 160 observations. The data reached an all-time high of 5,956.344 NA in Nov 2024 and a record low of 1,355.773 NA in Jan 2012. United States New York Stock Exchange: Index: MSCI US Sector Neutral Quality Index data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under Global Database’s United States – Table US.EDI.SE: New York Stock Exchange: MSCI: Monthly.

Graph and download economic data for Changes in Net Stock of Produced Assets: Nominal holding gains or losses (-): Neutral holding gains or losses: Private inventories (K100611A027NBEA) from 1951 to 2023 about gains/losses, stocks, inventories, Net, assets, private, GDP, and USA.

United States - Changes in Net Stock of Produced Assets: Nominal holding gains or losses (-): Neutral holding gains or losses: Fixed assets: Government was 473.85100 Bil. of $ in January of 2023, according to the United States Federal Reserve. Historically, United States - Changes in Net Stock of Produced Assets: Nominal holding gains or losses (-): Neutral holding gains or losses: Fixed assets: Government reached a record high of 1035.18700 in January of 2022 and a record low of 1.90500 in January of 1954. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Changes in Net Stock of Produced Assets: Nominal holding gains or losses (-): Neutral holding gains or losses: Fixed assets: Government - last updated from the United States Federal Reserve on May of 2025.

Overall, this project was meant test the relationship between social media posts and their short-term effect on stock prices. We decided to use Reddit posts from financial specific subreddit communities like r/wallstreetbets, r/investing, and r/stocks to see the changes in the market associated with a variety of posts made by users. This idea came to light because of the GameStop short squeeze that showed the power of social media in the market. Typically, stock prices should purely represent the total present value of all the future value of the company, but the question we are asking is whether social media can impact that intrinsic value. Our research question was known from the start and it was do Reddit posts for or against a certain stock provide insight into how the market will move in a short window. To solve this problem, we selected five large tech companies including Apple, Tesla, Amazon, Microsoft, and Google. These companies would likely give us more data in the subreddits and would have less volatility day to day allowing us to simulate an experiment easier. They trade at very high values so a change from a Reddit post would have to be significant giving us proof that there is an effect.

Next, we had to choose our data sources for to have data to test with. First, we tried to locate the Reddit data using a Reddit API, but due to circumstances regarding Reddit requiring approval to use their data we switched to a Kaggle dataset that contained metadata from Reddit. For our second data set we had planned to use Yahoo Finance through yfinance, but due to the large amount of data we were pulling from this public API our IP address was temporarily blocked. This caused us to switch our second data to pull from Alpha Vantage. While this was a large switch in the public it was a minor roadblock and fixing the Finance pulling section allowed for everything else to continue to work in succession. Once we had both of our datasets programmatically pulled into our local vs code, we implemented a pipeline to clean, merge, and analyze all the data. At the end, we implement a Snakemake workflow to ensure the project was easily reproducible. To continue, we utilized Textblob to label our Reddit posts with a sentiment value of positive, negative, or neutral and provide us with a correlation value to analyze with. We then matched the time frame of each post with the stock data and computed any possible changes, found a correlation coefficient, and graphed our findings.

To conclude the data analysis, we found that there is relatively small or no correlation between the total companies, but Microsoft and Google do show stronger correlations when analyzed on their own. However, this may be due to other circumstances like why the post was made or if the market had other trends on those dates already. A larger analysis with more data from other social media platforms would be needed to conclude for our hypothesis that there is a strong correlation.

A survey conducted in September 2023 on the upcoming holiday season revealed that roughly half of consumers were somewhat concerned or feeling neutral about stock-outs in the United States. 12 percent of total respondents were very concerned that their holiday shopping might be affected by product shortages.

Quant Fund Market Outlook

As of 2023, the global quant fund market size is estimated to be USD 1.2 trillion, with a projected CAGR of 8.5% leading to an anticipated market size of approximately USD 2.47 trillion by 2032. The rising adoption of algorithmic trading and advanced analytics stands out as a key growth factor driving this remarkable proliferation. The integration of artificial intelligence (AI) and machine learning (ML) to enhance trading strategies has been transforming the landscape, providing unprecedented opportunities for growth and efficiency gains.

One of the primary growth factors for the quant fund market is the increasing reliance on data-driven decision-making in financial markets. Institutional investors are progressively leveraging quantitative models to optimize their investment strategies, minimize risks, and capitalize on high-frequency trading opportunities. These sophisticated models, powered by AI and ML, allow for the processing of vast amounts of market data to uncover patterns and insights that would be nearly impossible to detect manually. This trend is expected to continue, further pushing the market's expansion.

Another significant factor contributing to the growth of the quant fund market is the technological advancements in computing power and data storage. The development of high-performance computing systems and the advent of cloud computing have enabled quantitative funds to process and analyze massive datasets in real-time. These technological innovations have not only enhanced the accuracy and efficiency of trading algorithms but also reduced the operational costs associated with running complex quantitative models. This evolution in technology is likely to sustain the market's growth trajectory in the coming years.

Furthermore, the increasing demand for diversification and risk management among investors is also driving the market's growth. Quantitative funds are designed to employ sophisticated strategies that aim to provide consistent returns while mitigating market risks. The ability to implement market-neutral strategies, statistical arbitrage, and trend-following techniques allows these funds to perform well even in volatile market conditions. This appeal of stable and diversified returns is attracting a broader range of investors, from institutional to retail, thereby expanding the market size.

The regional outlook for the quant fund market indicates that North America currently holds the largest market share, driven by the presence of numerous established quant funds and a mature financial ecosystem. However, the Asia Pacific region is anticipated to witness the highest growth rate over the forecast period, fueled by rapid economic development, increased adoption of advanced financial technologies, and a growing number of high-net-worth individuals seeking sophisticated investment solutions. Europe and Latin America are also expected to contribute significantly to the market growth, albeit at a slower pace compared to Asia Pacific.

Fund Type Analysis

The quant fund market can be segmented by fund type into equity funds, fixed income funds, multi-asset funds, and alternative funds. Within the equity funds segment, quantitative strategies have been particularly advantageous in identifying undervalued stocks and arbitrage opportunities, leading to a steady influx of investments. The application of machine learning algorithms to analyze stock performance and predict future trends has allowed equity-focused quant funds to generate consistent returns, attracting both institutional and retail investors.

Fixed income funds, on the other hand, have gained traction due to their ability to navigate the complexities of bond markets. Quantitative models in this segment are often employed to analyze interest rate movements, credit spreads, and economic indicators. The precision offered by these algorithms in predicting bond price movements has made fixed income quant funds a preferred choice for investors seeking stable returns with lower volatility compared to equity markets. Moreover, the inclusion of government and corporate bonds in their portfolios adds an additional layer of security for risk-averse investors.

Multi-asset funds, which combine equities, bonds, and other asset classes, have also seen significant growth. These funds leverage quantitative techniques to allocate assets dynamically based on market conditions. The ability to diversify across multiple asset classes while employing sophisticated risk management strategies makes multi-asset funds attractive to

纽约证券交易所：指数：MSCI US Sector Neutral Quality Index在04-01-2025达5,456.459NA，相较于03-01-2025的5,508.026NA有所下降。纽约证券交易所：指数：MSCI US Sector Neutral Quality Index数据按月更新，01-01-2012至04-01-2025期间平均值为2,704.364NA，共160份观测结果。该数据的历史最高值出现于11-01-2024，达5,956.344NA，而历史最低值则出现于01-01-2012，为1,355.773NA。CEIC提供的纽约证券交易所：指数：MSCI US Sector Neutral Quality Index数据处于定期更新的状态，数据来源于Exchange Data International Limited，数据归类于全球数据库的美国 – Table US.EDI.SE: New York Stock Exchange: MSCI: Monthly。

Graph and download economic data for Changes in Net Stock of Produced Assets: Nominal holding gains or losses (-): Neutral holding gains or losses: Fixed assets: Government (K100601A027NBEA) from 1951 to 2023 about gains/losses, stocks, fixed, Net, assets, government, GDP, and USA.

United States - Changes in Net Stock of Produced Assets: Nominal holding gains or losses (-): Neutral holding gains or losses: Fixed assets: Private: Residential was 844.05000 Bil. of $ in January of 2023, according to the United States Federal Reserve. Historically, United States - Changes in Net Stock of Produced Assets: Nominal holding gains or losses (-): Neutral holding gains or losses: Fixed assets: Private: Residential reached a record high of 1981.20200 in January of 2022 and a record low of -88.01600 in January of 2009. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Changes in Net Stock of Produced Assets: Nominal holding gains or losses (-): Neutral holding gains or losses: Fixed assets: Private: Residential - last updated from the United States Federal Reserve on June of 2025.