Skip to main content

Questions tagged [prediction]

The tag has no usage guidance.

Filter by
Sorted by
Tagged with
0 votes
1 answer
49 views

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 ...
Chris's user avatar
  • 1
0 votes
0 answers
53 views

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, ...
daniel dvali's user avatar
1 vote
1 answer
93 views

Neural network time series prediction tool [closed]

What are some of the state of the art time series prediction tool with neural network?
Hans's user avatar
  • 2,806
0 votes
0 answers
44 views

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, ...
wissam124's user avatar
0 votes
0 answers
42 views

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 ...
Filip's user avatar
  • 3
4 votes
0 answers
231 views

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 $\...
SL133's user avatar
  • 41
2 votes
1 answer
254 views

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. ...
QMath's user avatar
  • 249
1 vote
0 answers
52 views

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, $r_{t,t+5} = r_{t+1}+...+r_{t+5}$, ...
wlsdnwlsntus's user avatar
2 votes
2 answers
2k views

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{...
Lejoon's user avatar
  • 147
1 vote
1 answer
169 views

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 ...
ixodid's user avatar
  • 127
4 votes
1 answer
391 views

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 ...
mr_mm's user avatar
  • 103
2 votes
1 answer
316 views

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 ...
mr_mm's user avatar
  • 103
0 votes
0 answers
240 views

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 $...
manav's user avatar
  • 121
0 votes
1 answer
64 views

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 ...
rubikscube09's user avatar
1 vote
0 answers
90 views

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 ...
inf's user avatar
  • 41
1 vote
1 answer
138 views

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" ...
s5s's user avatar
  • 442
0 votes
1 answer
117 views

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 ...
s5s's user avatar
  • 442
8 votes
1 answer
646 views

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 ...
autoencoder's user avatar
0 votes
1 answer
427 views

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 ...
MaryM's user avatar
  • 1
0 votes
2 answers
204 views

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 ...
user708873's user avatar
0 votes
0 answers
34 views

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 ...
worldCurrencies's user avatar
3 votes
1 answer
418 views

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, ...
Alan's user avatar
  • 31
0 votes
1 answer
98 views

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 ...
CS1999's user avatar
  • 101
1 vote
0 answers
96 views

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....
user1682104's user avatar
0 votes
1 answer
176 views

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 ...
johndoedodgytoe's user avatar
0 votes
0 answers
75 views

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 ...
SkyWalker's user avatar
  • 101
0 votes
0 answers
363 views

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+...
Add's user avatar
  • 1,397
1 vote
0 answers
47 views

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. ...
yash agarwal's user avatar
0 votes
1 answer
149 views

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 ...
Duo's user avatar
  • 3
1 vote
0 answers
149 views

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 ...
PlatinumMaths's user avatar
2 votes
1 answer
101 views

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 ...
Maeaex1's user avatar
  • 121
1 vote
0 answers
202 views

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 ...
Daniel Bencik's user avatar
1 vote
1 answer
85 views

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 ...
Parseval's user avatar
  • 221
1 vote
0 answers
104 views

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 ...
PyRsquared's user avatar
0 votes
1 answer
137 views

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 ...
Bob hhhuh's user avatar
2 votes
1 answer
225 views

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 ...
Aditya Kulkarni's user avatar
0 votes
0 answers
62 views

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 ...
Roman's user avatar
  • 529
2 votes
0 answers
72 views

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 ...
Dylan Kerler's user avatar
1 vote
0 answers
70 views

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 ...
user3918807's user avatar
0 votes
1 answer
640 views

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 ...
BGa's user avatar
  • 169
0 votes
3 answers
833 views

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 ...
Vladimir Belik's user avatar
0 votes
1 answer
267 views

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 ...
CFM's user avatar
  • 1
2 votes
0 answers
92 views

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, ...
phdstudent's user avatar
  • 8,306
0 votes
1 answer
377 views

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 ...
Stat's user avatar
  • 141
0 votes
1 answer
135 views

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? ...
Alex Craft's user avatar
0 votes
0 answers
41 views

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.
kms's user avatar
  • 101
0 votes
3 answers
186 views

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 ...
develarist's user avatar
  • 3,000
1 vote
1 answer
50 views

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 ...
Satinder Pal Singh's user avatar
1 vote
1 answer
110 views

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 ...
olivia's user avatar
  • 209
0 votes
1 answer
132 views

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 ...
user avatar