We consider the execution of portfolio transactions with the aim of minimizing a combination of risk and transaction costs arising from permanent and temporary market impact. As an example, assume that you have a certain number of stocks that you want to sell within a given time frame. If you place this sell order directly to the market as it is, transaction costs may rise due to temporary market impact. On the other hand, if you split up into pieces in time, cost may rise due to volatility in the stock price

How to forecast volatility with GARCH. Professional options traders don’t make bets that stocks will go up or down. They look for mispricings in the market. Mispricing happens when the market price is different than what a model says the price should be.

This project aims to compare the performance of various open-source Large Language Models (LLMs) in predicting the price movements of cryptocurrencies and stocks. The goal is to assess how well these models handle price prediction tasks in financial markets with different levels of volatility, such as cryptocurrencies and traditional stocks.

Time Series Forecasting Method in Finance and Stocks industry use many frameworks serving MLOPs application.

In financial markets, volatility captures the amount of fluctuation in prices. For trading firms like Optiver, accurately predicting volatility is essential for the trading of options, whose price is directly related to the volatility of the underlying product.In this Kaggle competition,we had built models that predict short-term volatility for hundreds of stocks across different sectors. Our models will be evaluated against real market data collected in the three-month evaluation period after training.

The financial market is a dynamic and composite system where people can buy and sell currencies, stocks, equities and derivatives over virtual platforms supported by brokers. Stock markets are affected by many factors causing the uncertainty and high volatility in the market. Although humans can take orders and submit them to the market, automated trading systems (ATS) that are operated by the implementation of computer programs can perform better and with higher momentum in submitting orders than any human. Since most of the dealings in the markets are done by automated systems, it has now been well established that training the past data can help us in finding patterns in the movement of the markets which can be used to predict the future prices. If implemented successfully with a higher accuracy than existing systems, it could turn into a financial support system with minimal amount of risk. We will be using a Random Forest Classification algorithm as the dataset that we train is completely discrete and we will be using several indicators to calculate the data on which the training will be performed.

This repository builds a volatility prediction tool using ultra-high-frequency data to forecast future price swings for over 100 stocks. It processes order book data, computes key statistics like BidAskSpread and weighted average price, and applies time series models to deliver interpretable predictions that help traders understand market behavior.

This Streamlit app provides an interactive interface to analyze and visualize the performance of various tech stocks over a specified date range. The app leverages historical stock data from Yahoo Finance and offers insights into stock prices, moving averages, and volatility.

Overview Smart beta has a broad meaning, but we can say in practice that when we use the universe of stocks from an index, and then apply some weighting scheme other than market cap weighting, it can be considered a type of smart beta fund. A Smart Beta portfolio generally gives investors exposure or "beta" to one or more types of market characteristics (or factors) that are believed to predict prices while giving investors a diversified broad exposure to a particular market. Smart Beta portfolios generally target momentum, earnings quality, low volatility, and dividends or some combination. Smart Beta Portfolios are generally rebalanced infrequently and follow relatively simple rules or algorithms that are passively managed. Model changes to these types of funds are also rare requiring prospectus filings with US Security and Exchange Commission in the case of US focused mutual funds or ETFs.. Smart Beta portfolios are generally long-only, they do not short stocks. In contrast, a purely alpha-focused quantitative fund may use multiple models or algorithms to create a portfolio. The portfolio manager retains discretion in upgrading or changing the types of models and how often to rebalance the portfolio in attempt to maximize performance in comparison to a stock benchmark. Managers may have discretion to short stocks in portfolios. Imagine you're a portfolio manager, and wish to try out some different portfolio weighting methods. One way to design portfolio is to look at certain accounting measures (fundamentals) that, based on past trends, indicate stocks that produce better results. For instance, you may start with a hypothesis that dividend-issuing stocks tend to perform better than stocks that do not. This may not always be true of all companies; for instance, Apple does not issue dividends, but has had good historical performance. The hypothesis about dividend-paying stocks may go something like this: Companies that regularly issue dividends may also be more prudent in allocating their available cash, and may indicate that they are more conscious of prioritizing shareholder interests. For example, a CEO may decide to reinvest cash into pet projects that produce low returns. Or, the CEO may do some analysis, identify that reinvesting within the company produces lower returns compared to a diversified portfolio, and so decide that shareholders would be better served if they were given the cash (in the form of dividends). So according to this hypothesis, dividends may be both a proxy for how the company is doing (in terms of earnings and cash flow), but also a signal that the company acts in the best interest of its shareholders. Of course, it's important to test whether this works in practice. You may also have another hypothesis, with which you wish to design a portfolio that can then be made into an ETF. You may find that investors may wish to invest in passive beta funds, but wish to have less risk exposure (less volatility) in their investments. The goal of having a low volatility fund that still produces returns similar to an index may be appealing to investors who have a shorter investment time horizon, and so are more risk averse. So the objective of your proposed portfolio is to design a portfolio that closely tracks an index, while also minimizing the portfolio variance. Also, if this portfolio can match the returns of the index with less volatility, then it has a higher risk-adjusted return (same return, lower volatility). Smart Beta ETFs can be designed with both of these two general methods (among others): alternative weighting and minimum volatility ETF.

Ang, Hodrick, Xing, and Zhang (2006) investigate the impact of aggregate volatility on expected stock returns. They explore how market volatility influences investors' expectations of future returns and the risk-return trade-off, suggesting that volatility is a systematic risk factor priced into stocks.

Ang,Hodrick, Xing and Zhang (2006) explore how market volatility influences investors' expectations of future returns and the risk-return trade-off, suggesting that volatility is a systematic risk factor priced in stocks. Our code analyses the investment strategy based on idiosyncratic volatility in Australia using data from https://jkpfactors.com

The volatility surface is a 3D plot showing the implied volatilities of a stocks OR index/other options that are listed on it across different strike prices and expirations.

This project analyse U.S. stocks and detects underpriced and overpriced options using the Black-Scholes pricing model along with Implied Volatility (IV) and Historical Volatility (HV) spread analysis. Upon detecting a mispricing, the system applies a position sizing strategy and simulates returns on a $1 million portfolio.

Periodic volatility is healthy and good for the markets; it keeps the herd mentality in control Global currency shifts, a slow economic recovery and richer valuations have led to volatility in the Indian markets intermittently. Use these to build your corpus. For some time now, the Indian equity market has been on a batting wicket, scoring ever higher. These periods of batting excellence, though, have been scarred by dry spells. In other words, the market often witnessed volatility. There were many reasons behind this. Recently, the Reserve Bank of India’s neutral credit policy, with a hawkish tone, saw a immediate reaction in the stock market. As further rate cuts were expected, markets were disappointed. Stock market reactions to events such as this are turning commonplace. Currency movements and a stronger dollar are other factors that are driving greater volatility globally. Cross-currency movements—one currency getting stronger and another weaker—against the Indian rupee is also playing occasional havoc in the markets. Despite the fact that the Indian rupee is one of the better behaved currencies, helping toward robust fund flows, a strong dollar and possible interest rate tightening in the West in 2015 have also been responsible for the intermittent market volatility. Even the recent earnings season for Indian companies wasn’t as optimistic as expected. This quarter, the financial year 2015’s estimated earnings growth ended on a low note with consensus earnings growth estimates for BSE 100 companies dropping to 10.1%, down 600 basis points (bps) since the end of the last earnings season (as on 17 November 2014). One basis point is one-hundredth of a percentage point. Even though earnings have been showing signs of bottoming out, equity prices are a shade higher. Fortunately, valuations haven’t yet gone overboard; though they are not necessarily inexpensive. Nevertheless, because of the richer valuations, stocks are going through corrections The good in volatility The thing to remember is that periodic volatility is healthy and good for the markets; it keeps the herd mentality in control. It provides opportunities to investors who are on the look out for good entry points, which arise only during volatile times. That’s the beauty of the market. Investors can accumulate high quality assets on the decline as prospects for equity markets are rosy for the period 2016-2018. Over the next few years, the Indian economy is expected to bounce back from its slow-moving growth rates. While the new gross domestic product (GDP) figures look good (showing growth rates of 7.4%), the investment cycle is yet to pick up to the desired pace. But we are optimistic that the growth cycle will begin to increase sooner rather than later. In a few years, the Indian economy is poised to record some of the highest growth rates in the world. In the other big economy (China), growth rates are plateauing. The euro zone economy is struggling, while the rest of the world is much below peak level growth rates. The only country that is turning out decent growth numbers is the US—and that benefits India immensely. Deflation is clear in the global economy, especially in commodity prices such as oil and metals. With oil at $60-65 a barrel, Indian economy saves about $45 billion a year And if we get the supply chain right, interest rates could go down much more significantly. This would be very good for the capital expenditure cycle and the return on equity in the long run. Most Indian investors are still under-invested in equity. So, high volatility for any reason, whether global currency or supply of paper through equity issuances such as divestment or institutional placements, it’s an opportunity to pick up Indian equities for the long term. Corrections aren’t something that one should worry about if you are an Indian equity investor. In a unique position Purely from an investing point of view, the recent correction in technology stocks gave one the opportunity to invest in the sector with a three-year role. Among the cyclical sectors, the correction in commodities, too, provides an opportunity to invest with a three-year horizon. We all know that the stock market is not a one-way street that goes up all the time. But, unfortunately, most of us don’t act on it. Those who do are likely to reap significant rewards in the longer run. Investors who feel they have missed the bus can, and should, look forward to market corrections. It will, however, be a bumpy ride. That’s because though we have an economy that’s on the cusp of growth, concerns persist about large pockets of global economic trouble. But again, given that the growth dynamics in India have changed in the recent past with oil at $65 and further economic reforms on the anvil, Indian equities will deliver better returns than other investments. Therefore, whenever the market runs into rough weather, ensure that you use the opportunity to stuff some sound assets in your kitty. This article has also been published in Mint on 10th March 2015

Quantitative analysis of Indian stocks using Python — price trends, returns, and volatility via yFinance and Matplotlib.

A Python script that analyzes stock prices and produces a list of stocks based on historical growth rates and price volatility.

Combine multiple factors simultaneously: 1. Momentum (price trends) 2. Value (cheap stocks) 3. Quality (profitable companies) 4. Size (small vs large companies) 5. Volatility (low risk stocks)

BSM option pricing engine with Greeks, implied volatility surface, delta/gamma hedging, and 95%/99% VaR analysis on NSE-listed stocks.

A Python-based stock portfolio analyzer using Numpy. Analyzes 3 stocks with price statistics, volatility, daily returns, correlation analysis, buy/sell signals and portfolio summary.

Quantitative analysis of Federal Reserve rate hikes (2022-2023) and their impact on Growth vs. Value stocks. Features Event Studies, GARCH(1,1) volatility modeling, and LSTM price forecasting using Python.

Stock data importing from Yahoo and Tiingo, resampling in terms of quarters, months, weeks, constructing moving windows, checking volatility of stocks, rolling means, comparing performances of different stocks using subplots, data preprocessing, model building with deep learning and predicting stock prices for future.

This project analyzes historical financial market data using Python to understand price trends, returns, and volatility in both traditional stocks and cryptocurrencies. In addition, basic machine learning models are applied to cryptocurrency data to predict short-term price direction.

This project analyzes 5 years of daily stock data for 10 companies using Python and yFinance. It calculates monthly averages for price, trading volume, dollar volume, returns, and risk. It also identifies the highest return and volatility stocks, plus top/bottom decile volatility months.

Part 1: Given user inputs for current stock prices, stock volatility, risk-free interest rate, strike price, u & d (up & down factors for stocks), size of the time steps, type of option – call/ put (oType), the code computes the value for a European call/ put option. Part 2: Choosing IBM stocks and the corresponding options maturing before the next dividen paying date, the code calibrates a multi-period binomial model. Historical stocks data was used to calibrate volatility (σ), up-down factors (u & d) and risk-neutral probabilities (pTilda).

Black-Scholes model to price options on various underlying assets, such as stocks, bonds, and currencies. I also calculated the greeks of these options, which are measures of their sensitivity to changes in the underlying asset price, volatility, interest rates, and time to expiration.Ability to use the Black-Scholes model to price options

EDA project on top 25 Pakistan Stock Exchange (PSX) stocks to analyze price trends, returns, volatility, and correlations. Interactive visualizations generate market insights. A Streamlit web app is built for dynamic exploration and deployed on Streamlit for public use.

This project provides a comprehensive financial analysis of over 500 S&P 500 stocks from 2013 to 2018. It uses a robust Python-to-SQL data pipeline to clean historical price data and calculate key performance metrics (Cumulative Return, Volatility, etc.).

machine learning web application using Python's FastAPI and scikit-learn to predict S&P 500 stock price trends and cluster stocks based on average annual returns and volatility. Utilised the MVC design pattern to structure the application effectively. Implemented a decision tree classifier with 84% accuracy.

Portfolio analysis program implemented in Python using pandas, yfinance and numpy libraries. The program will generate a price chart of the stocks in the portfolio and the cumulative return chart of the portfolio. It also calculates the average return, average annualized return and volatility of the portfolio.

• Implemented ARCH and GARCH models to predict the return series and volatality of S&P 500 and The Walt Disney Company stocks • Used Partial auto corelation function and Auto corelation function to dervive the seasonality and number of autoregressive terms • Predicted volatility and represented returns for next 7 days by training the model with the help of stock prices data of last 20 years