Questions tagged [prediction]
The prediction tag has no usage guidance.
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How to predict a portfolio's reversion?
Sorry if this has been asked before. I've been baffled by a question I'm facing.
Assuming I know there are some certain demands for some stocks in near future, and I put them in a basket as a ...
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Using regression for binomial prediction of tomorrow's return
I completed a challenge which asks the user to predict tomorrow's market return. The data available is prices data and the model must be logistic regression. They call it "machine learning" ...
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Differences vs ratios
High, I am working on an exercise which involves performing a regression analysis to predict market direction (e.g. up or down). I am using daily OHLCV data. I've created various factors from the ...
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Time series strategy versus cross section strategy?
Suppose we have a universe of $n$ stocks, and for each time period $t$ we have $n$ predictions for their future returns. Now we can calculate the information coefficient for our predictions in two ...
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evaluate the predictive power of a signal to predict stock price - interview question
I am a young Statistics graduate.
A few days ago as an interview question, I have been asked to evaluate the predictive power of a Signal time series (supposedly output by an Artificial Intelligence ...
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Is there any utility to being able to predict an assets current price?
I was playing around with some models, and I'm able to predict a stock's current price based on the current prices of other stocks. This model is extremely accurate, although I can't see any use of ...
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How to predict what stage of business cycle we are currently in based off of unemployment indicators
I am trying to predict what part of the business cycle (Early, Mid, Late 1, Late 2) we are currently in by looking at unemployment indicators.
Qualitatively, I've reasoned that:
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Early
Mid
Late 1
...
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Paper on returns from perfect market timing?
I'm looking for a (free) paper I read which showed that even a "perfect" market timing strategy wasn't very good compared to buy-and-hold. There were some restrictions to the timing, ...
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Lazypredict using Multiple Tickers
I am trying to use multiple tickers close prices , create some technical indicators and run lazy predict to get multiple results of Machine Learning performance models.
I have cleaned the data somehow....
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In stock prediction with LSTM, is there a need to get a dataset for a specific time period in order to predict future close price?
I am currently trying to predict the close price of the TSLA stock for March 2022 using LSTM model. Initially, I was using TSLA stock data starting from 2012 to of course March 2022. However, I was ...
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How to reduce validation error for LSTM while working on time series data?
I am currently building a multivariate LSTM for predicting the close price of the next 3 days. I have tried changing parameters such as learning rate, number of layers (and neurons), activation ...
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Adapted Roll measure implementation
I'm currently trying to implement the roll measure adapted by Easley et al. (2020, p. 22).
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3345183
The adapted roll measure is given by the eq below....
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What kinds of data should be curated for day trading?
From previous research about data curation with research papers, it seems to me that most algorithmic trading systems (at least in regards to day trading) solely use historical price data- but I'd be ...
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Disecting a log diff transformation for time series analysis and prediction
I have been working in a predictive ML model that uses financial time-series as predictor variables. In one of the academic papers I used as reference, and to do feature engineering for building the ...
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What is the meaning of adding an non-binary interaction variable to a regression?
When examining whether the impact of laws on Y differently in developed countries is to add an interaction variable.
The general equation is:
Leniency_law is a variable of interest in a Differentce-in-...
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Clarifying statement on volatility, stock returns prediction, and beta
I am trying to interpret a statement about volatility and stock returns made during an interview which I really do not understand. The original task was as follows:
Given a time series of the ...
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Hidden Markov Model Stock Prediction Next Level
I was able to fit HMM Model in Python on stocks data. I have completed the training and testing part. The overall fit looks good. However, I have a question, I am not able to predict the next "t+...
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Presence of underestimation bias in consensus earnings predictions
I am working on a financial data that entails forecasted revenue a company generates over a fiscal quarter and the actual revenue for that quarter.
...
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Book/Material recommendation - Stock Price Forecasting using AI tools
I am looking for a book, which covers the following topics:
stock price prediction using Artificial Neural Network,
stock price prediction using LSTM,
stock price prediction using linear/non-linear ...
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stock price trend classification using Random Forest in sklearn
I have created a random forest classification model in skicit-learn, but I am unsure how to finalise my forecast.
I have built the model and it is showing good results on the testing data. I get a ...
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Prediciting outperformance - choice of statistical design?
I want to predict relative outperformance between a stock and an associated benchmark index using statistical time-series models (e.g. ARIMA) and some exogenous variables (day of the week, corporate ...
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Current research in price prediction
I am returning to studying the markets after ten years spent in banking. I would like to ask for directions, what approaches to price prediction are currently used - I want to catch up quickly.
Papers ...
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Find best linear predictor of $X_2$ given $1, X_1$
I'm having a problem calculating the best linear predictor of a time series. I'm using the book Brockwell-Davis 2016 - Introduction to Time Series and Forecasts. First let me write down one notational ...
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Predicting smoothed returns
Due to the extremly low ratio of signal to noise in financial data, predicting raw returns is very difficult.
If we smooth out the price time series, say by an EWMA, and then calculate returns on this ...
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Non-fixed stationary "conversion"
Dear users of StackExchange,
I was wondering why the log returns of a fixed period of time is such a common use in "transforming" a time series into a more stationary one?
I thought that ...
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Using candlesticks for Stock price direction prediction
I am working on a college project wherein I want my machine learning model to predict the one-day-ahead direction of a given stock (i.e. whether the closing price of the stock would rise or fall as ...
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How to use machine learning to generate optimal allocations for an instrument?
What is the idea behind using Machine Learning in finance? Let's assume that we have just one instrument given by its prices. At a given moment of time, we can "compress" the available ...
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Why are prediction markets based on logarithms when a linear solution can suffice?
For example, take a binary outcome; A coin toss, heads or tails.
If heads, then those that picked heads receive \$1 and tails receive \$0.
To quote the prices for each bet Hanson's LMSR uses ...
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Please help me understand this dataset regarding stock prices
I am supposed to predict column E but I cannot figure out what any of these columns mean. The information provided with the dataset is as follows:
column A: past 28 week slope value
column B: past 48 ...
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Feature engineering for mid-price prediction - quickly changing features
I'm training a fully-connected feed-forward neural network on HFT (limit order book) data to predict the midprice at timepoint $t+\Delta t$ (assuming that $t$ is the current moment, and $\Delta t$ is ...
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Consistent offset/lag in time-series prediction using Neural Network (all code provided)
I'm using a neural network (keras package) to predict Bitcoin prices 48 hours in advance. The issue is that for some reason, my predictions are "correct" but they are lagging behind the true ...
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Reintegrating Fractionally Differentiated Time Series Prediction
I am working on a supervised learning approach to Time Series Regression, and am currently investigating fractionall differentiation (optimizing the stationarity/information tradeoff) discussed ...
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Garch models - are they useful for hedging? If so how?
I understand that Garch models are useful to predict volatility. But are they useful for hedging in practice? If I want to hedge volatility, why shouldn't I just use a Variance Swap?
In other words, ...
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Predict Log Stock Return Direction and Trading Strategy
The $k$ period log return is defined as $$r_{t}(k)=log(S_{t}/S_{t-k}),$$ Where $S_{t}$ is the stock closing price at time $t$. For argument sake, assume that by time I mean a stock trading day and ...
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Is option surface same as future price probability surface?
Let's consider the Option Chain for the Stock. There are two 3D surfaces representing the probability of the future stock price and the option prices. I wonder if they are representing the same thing?
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Tradeoffs of using Loess regression to fit random walks
I am curious if anyone has had much experience attempting to predict random walks using Loess regression or a variant of local statistical methods.
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Does asset volume, rather than asset returns, predict performance?
Asset returns are the most common data type used in finance. They are derived from closing price data. Ordinary level 1 data for stocks not only consists of closing prices, but also gross volume ...
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Mutual fund rating predictions
I am working on a dataset with aim to predict the MF ratings. There are cols like, 10 yr, 7 yr, 5 yr etc returns. I also have commencement date of MFs, the question is there are MFs with commencements ...
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How to use multi-periods and mult-factors to predict stock price by linear regression?
Give data in $t_n$ denoted by $[x_1^n, x_2^n, ... x_d^n]$ and label $y_n$ to be predicted. We can just train a $d$-dimensional linear regression $y_n=\sum b_ix_i^n$ to make a prediction. However, I ...
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If a security has many options expiring on a day, can you predict its price direction?
r/wallstreetbets post alleges that because ~$1.9 Trillion USD of SPY call options expire on 3/20, the price of SPY will skyrocket on 3/20.
Is this correct? What can be deduced? I'm not expecting to ...
user31928
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Predicting time series based on another
This is more of a generic question, but I'm sure it has a best answer/methodology which is what I'm trying to reach. I'm trying to figure out a solid line of thought when looking at a time series X ...
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Relationship between Data Size and Arima Prediction Interval Width?
When we use Arima model to acquire Interval Predictions, will the width of prediction intervals decrease if we use more data (longer history) to fit the model?
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Updated Time Series Prediction Model When acquiring new data Points - Basic Question
Suppose I have a Time Series Model (assume ARIMA) and use it to make one-step ahead prediction.
If I acquire a new data point, (for example I was originally using the first 100 days to fit an Arima ...
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Replication of the paper: "A Comprehensive Look at the Empirical Performance of Equity Premium Prediction"
I recently replicated the paper "A Comprehensive Look at the Empirical Performance of Equity Premium Prediction" and found out that my estimation of the equity premium differs from the data provided ...
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Question is about the data in the paper: "A Comprehensive Look at The Empirical Performance of Equity Premium Prediction"
I would like to ask a question if you download the data from the Amit Goyal website:
http://www.hec.unil.ch/agoyal/
You will see that there are two columns "CRSP_SPvw" "CRSP_SPvwx", they are ...
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Proper Data Partitioning For Building a Forecasting Model
Goal: A team and I are looking to build a model that performs a predictive action for the state of the market on day T + n, using the data at hand on day ...
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How to weigh computational cost of updating an online predictive model for latency-constrained trading (e.g., market making, HFT)?
Say one has a predictive online model for market making or HFT (or just for anything strictly latency-constrained). In my specific example, I start with a Gaussian distribution over the "true value" ...
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time series data modeling for deep learning
what is the best format to feed the input data, which are time series with varying density over time, to a deep learning network, while at any iteration we want to feed a batch of data including a ...
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Which technique determines if var x1 leads var y? Assuming var x1 may need to be transformed
Suppose I want to predict future changes in variable y (stock price over time). I notice that variable x1, inverted and delayed three months, tends to lead y. Which technique can I use to find other ...
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Beginner FFT (Fourier) transforms on closing prices for Apple
I don't know math very well, but I have been programming for many years.
I would like to use FFT as a parameter to a ML model. The FFT is diving down sharply. I tried many stocks and its the same.
...
