Andrew C. Pollock

591 total citations
21 papers, 396 citations indexed

About

Andrew C. Pollock is a scholar working on Management Science and Operations Research, General Economics, Econometrics and Finance and Economics and Econometrics. According to data from OpenAlex, Andrew C. Pollock has authored 21 papers receiving a total of 396 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Management Science and Operations Research, 11 papers in General Economics, Econometrics and Finance and 11 papers in Economics and Econometrics. Recurrent topics in Andrew C. Pollock's work include Forecasting Techniques and Applications (12 papers), Monetary Policy and Economic Impact (11 papers) and Decision-Making and Behavioral Economics (9 papers). Andrew C. Pollock is often cited by papers focused on Forecasting Techniques and Applications (12 papers), Monetary Policy and Economic Impact (11 papers) and Decision-Making and Behavioral Economics (9 papers). Andrew C. Pollock collaborates with scholars based in United Kingdom and Türkiye. Andrew C. Pollock's co-authors include Dilek Önkal, Mary E. Thomson, M. Sinan Gönül, Paul Goodwin and Rory MacLean and has published in prestigious journals such as European Journal of Operational Research, Journal of Business Research and Decision Support Systems.

In The Last Decade

Andrew C. Pollock

20 papers receiving 376 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Andrew C. Pollock United Kingdom 9 181 119 83 78 66 21 396
Veronika Köbberling Netherlands 6 154 0.9× 376 3.2× 89 1.1× 39 0.5× 332 5.0× 7 553
Olivier Gossner France 12 278 1.5× 56 0.5× 124 1.5× 35 0.4× 256 3.9× 40 483
Alessandra Cillo Italy 8 124 0.7× 142 1.2× 39 0.5× 21 0.3× 152 2.3× 15 337
Amit Kothiyal Germany 9 75 0.4× 115 1.0× 39 0.5× 34 0.4× 111 1.7× 14 248
Alexander Matros United States 12 182 1.0× 51 0.4× 267 3.2× 31 0.4× 194 2.9× 36 476
Emre Soyer Spain 8 55 0.3× 80 0.7× 34 0.4× 38 0.5× 34 0.5× 16 274
Colin Stewart Canada 8 123 0.7× 67 0.6× 78 0.9× 9 0.1× 123 1.9× 22 296
Ernst Diehl United States 3 205 1.1× 44 0.4× 32 0.4× 71 0.9× 25 0.4× 6 350
Ryo Okui Japan 13 107 0.6× 67 0.6× 105 1.3× 31 0.4× 285 4.3× 43 685
Ronald Peeters Netherlands 11 201 1.1× 44 0.4× 131 1.6× 16 0.2× 182 2.8× 71 405

Countries citing papers authored by Andrew C. Pollock

Since Specialization
Citations

This map shows the geographic impact of Andrew C. Pollock's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Andrew C. Pollock with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Andrew C. Pollock more than expected).

Fields of papers citing papers by Andrew C. Pollock

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Andrew C. Pollock. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Andrew C. Pollock. The network helps show where Andrew C. Pollock may publish in the future.

Co-authorship network of co-authors of Andrew C. Pollock

This figure shows the co-authorship network connecting the top 25 collaborators of Andrew C. Pollock. A scholar is included among the top collaborators of Andrew C. Pollock based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Andrew C. Pollock. Andrew C. Pollock is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Thomson, Mary E., et al.. (2023). The anti-money laundering risk assessment: A probabilistic approach. Journal of Business Research. 162. 113820–113820. 11 indexed citations
2.
Thomson, Mary E., et al.. (2021). QUANTILE PROBABILITY PREDICTIONS: A DEMONSTRATIVE PERFORMANCE ANALYSIS OF FORECASTS OF US COVID-19 DEATHS. Research Output (Edinburgh Napier University). 9(2). 139–163. 1 indexed citations
3.
Thomson, Mary E., et al.. (2021). Composite Quantile Probability Predictions: Performance and Coherence Analysis of US COVID-19 Confirmed Infection Cases. International Journal of Scientific Research and Management (IJSRM). 9(12). 471–489.
4.
MacLean, Rory, et al.. (2018). Threshold point utilisation in juror decision-making. Psychiatry Psychology and Law. 26(1). 110–128. 3 indexed citations
5.
Thomson, Mary E., Andrew C. Pollock, Dilek Önkal, & M. Sinan Gönül. (2018). Combining forecasts: Performance and coherence. International Journal of Forecasting. 35(2). 474–484. 33 indexed citations
6.
Pollock, Andrew C., et al.. (2010). Evaluating strategic directional probability predictions of exchange rates. International Journal of Applied Management Science. 2(3). 282–282. 3 indexed citations
7.
Önkal, Dilek, Paul Goodwin, Mary E. Thomson, M. Sinan Gönül, & Andrew C. Pollock. (2009). The relative influence of advice from human experts and statistical methods on forecast adjustments. Journal of Behavioral Decision Making. 22(4). 390–409. 229 indexed citations
8.
Pollock, Andrew C., et al.. (2008). Using Weekly Empirical Probabilities in Currency Analysis and Forecasting. ResearchOnline. 5(2). 26–55. 1 indexed citations
9.
Pollock, Andrew C., et al.. (2005). Performance evaluation of judgemental directional exchange rate predictions. International Journal of Forecasting. 21(3). 473–489. 22 indexed citations
10.
Thomson, Mary E., et al.. (2004). The influence of the forecast horizon on judgemental probability forecasts of exchange rate movements. European Journal of Finance. 10(4). 290–307. 10 indexed citations
11.
Pollock, Andrew C., et al.. (2003). The Influence of Trend Strength on Directional Probabilistic Currency Predictions. SSRN Electronic Journal. 1 indexed citations
12.
Goodwin, Paul, et al.. (2003). Feedback-labelling synergies in judgmental stock price forecasting. Decision Support Systems. 37(1). 175–186. 16 indexed citations
13.
Thomson, Mary E., et al.. (2002). The influence of trend strength on directional probabilistic currency predictions. International Journal of Forecasting. 19(2). 241–256. 13 indexed citations
14.
Pollock, Andrew C., et al.. (1999). Evaluating predictive performance of judgemental extrapolations from simulated currency series. European Journal of Operational Research. 114(2). 281–293. 3 indexed citations
15.
Önkal, Dilek, et al.. (1997). Currency forecasting: an investigation of extrapolative judgement. International Journal of Forecasting. 13(4). 509–526. 10 indexed citations
16.
Pollock, Andrew C., et al.. (1996). The quality of bank forecasts: The dollar-pound exchange rate, 1990–1993. European Journal of Operational Research. 91(2). 306–314. 6 indexed citations
17.
Pollock, Andrew C., et al.. (1996). An application of probability judgement accuracy measures to currency forecasting. International Journal of Forecasting. 12(1). 25–40. 17 indexed citations
18.
Pollock, Andrew C.. (1990). Forecasting quarterly movements of the lira/pound-sterling exchange rate: Random walks, drift, seasonality and variable parameters. Decisions in Economics and Finance. 13(1-2). 23–42. 4 indexed citations
19.
Pollock, Andrew C.. (1989). The time series characteristics of quarterly nominal and real lira/pound-sterling exchange rate movements, 1973–1988. Decisions in Economics and Finance. 12(1). 167–193. 4 indexed citations
20.
Pollock, Andrew C.. (1989). A model of UK real effective exchange rate behaviour. Applied Economics. 21(12). 1563–1587. 3 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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