Questions tagged [prediction]
The prediction tag has no usage guidance.
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Predict future Implied Volatility Surface with LSV models
From my understanding, Local Stochastic Volatility (LSV) models (such as the Heston-LSV for instance) are ones of the most used diffusion models used for exotic pricing. One of their advantages (by ...
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Predictive Forecast (Close, 14)
I've been following an asset wherein a "R-squared predictive forecast (close, 14)" is posted online each day. On some days, this figure is extremely high, like .92.
Exactly what is the ...
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Boosting models for algo trading
I’m currently working on a xgboost model to predict the price change above or below a given percentage between a candle’s open price and the next candle’s close price. I use a wide range of features, ...
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Neural network time series prediction tool [closed]
What are some of the state of the art time series prediction tool with neural network?
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Predict a company business classification
I am trying to predict whether companies belong to a universe considered by an index provider for a particular thematic index using natural language processing techniques.
In this particular example, ...
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Closed cycle of pairs in pairs trading
Suppose I am trading cointegrated pairs $A_1A_2, A_2A_3, \ldots A_{k-1}A_k, A_kA_1$ and I got a signal to
long $A_1$, short $A_2$;
long $A_2$, short $A_3$;
$\cdots$
long $A_k$, short $A_1$.
How ...
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258
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Principal Portfolios Prediction Matrix estimation (Bryan Kelly)
I have recently discovered Bryan Kelly's paper on Principal Portfolios (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3623983) and had some doubts about the prediction matrix $\Pi$. He defines $\...
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Is there a general approach to predicting future (vanilla) option prices in practice?
I realize that this question may be verging on asking for the proprietary/"secret", so if suggestion of a general approach that doesn't divulge details isn't really possible, I understand.
...
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Testing predictability of a proposed predictor in case of multiple returns
Say I have a T daily observations for the last ten years on a new predictor $x_t$ which I think is a predictor of the expected weekly return on the stock market, $r_{t,t+5} = r_{t+1}+...+r_{t+5}$, ...
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One-day-ahead prediction of S&P500 with Temporal Convolutional Networks
I'm trying to predict the one-day ahead movement of the S&P 500 with Temporal Convolutional Networks 1 to capture some "memory".
I use daily close data with the loss function $\mathrm{...
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Understanding how markets predict BoC's policy interest rate decisions
I read in the newspaper things like,
Interest rate swaps, which are based on market expectations about future rate decisions, are pricing in at least one Bank of Canada rate cut later this year, and ...
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Continuous prediction vs Event-based predictions
When making a high-frequency or mid-frequency prediction on an assets return, what are the advantages and disadvantages of making a continuous prediction vs a prediction that only fires on a ...
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Constructing a mid using signals from another asset
When delta-neutral market making it is important to construct a mid price. Often the mid price of the asset you are trading is influenced by another (correlated) asset. What methodologies would you ...
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combining forecasts at different time horizons
I define a prediction of return of an asset as the following: at time $t=0$, I use my data and output that I expect the asset to make the following returns (in expected value) in the next n intervals $...
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Averaging Results Across Regressions due to Periodicity/Overlaps
Given data that arrives at a daily frequency, I aggregated it to a weekly frequency, and estimated an OLS regression on it. Given that there are roughly 5 trading days per week, I can construct 5 ...
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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:
.
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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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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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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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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